{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "recreating-spiral.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/reiinakano/neural-painters/blob/master/notebooks/recreating_spiral.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "qqI4x4o6SuSt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Optionally connect to Google Drive"
      ]
    },
    {
      "metadata": {
        "id": "ESVcsBip1IIZ",
        "colab_type": "code",
        "outputId": "cd62f803-5ed2-4e3e-e9ba-aa710c8ac3eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        }
      },
      "cell_type": "code",
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9P5f_uzAq_5g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Install MyPaint and other dependencies"
      ]
    },
    {
      "metadata": {
        "id": "bAWyePtkn3av",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Install mypaint\n",
        "!apt-get update\n",
        "!apt-get install libjson-c-dev libgirepository1.0-dev libglib2.0-dev\n",
        "!apt-get install autotools-dev intltool gettext libtool\n",
        "!apt-get install swig python-setuptools gettext g++\n",
        "!apt-get install -y libgtk-3-dev python-gi-dev\n",
        "!apt-get install -y libpng-dev liblcms2-dev libjson-c-dev\n",
        "!apt-get install -y gir1.2-gtk-3.0 python-gi-cairo\n",
        "!apt-get install scons\n",
        "\n",
        "!wget https://github.com/mypaint/libmypaint/releases/download/v1.3.0/libmypaint-1.3.0.tar.xz\n",
        "!tar -xvf libmypaint-1.3.0.tar.xz\n",
        "!mv libmypaint-1.3.0 libmypaint\n",
        "\n",
        "!cd libmypaint && ./configure && make install\n",
        "\n",
        "!wget https://github.com/mypaint/mypaint/releases/download/v1.2.1/mypaint-1.2.1.tar.xz\n",
        "!tar -xvf mypaint-1.2.1.tar.xz\n",
        "!mv mypaint-1.2.1 mypaint\n",
        "!cd mypaint && scons && scons install\n",
        "\n",
        "!ldconfig\n",
        "\n",
        "!pip install ipdb tqdm pathlib cloudpickle matplotlib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nx2C15rB4Huz",
        "colab_type": "code",
        "outputId": "d8849eb1-e10a-46ad-c1da-0dd93247ae3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install future-fstrings"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting future-fstrings\n",
            "  Downloading https://files.pythonhosted.org/packages/d5/10/de62670513b7b2e7de32bbabb662cbc05ccee49fadf6f69725388df780e8/future_fstrings-1.0.0-py2.py3-none-any.whl\n",
            "Collecting tokenize-rt; python_version < \"3.6\" (from future-fstrings)\n",
            "  Downloading https://files.pythonhosted.org/packages/43/4b/c5df89ff5b38afffc04fb208c9b1fce30c1426788a368d7039b4cbcf524e/tokenize_rt-2.2.0-py2.py3-none-any.whl\n",
            "Installing collected packages: tokenize-rt, future-fstrings\n",
            "Successfully installed future-fstrings-1.0.0 tokenize-rt-2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1ueV31ATgbQp",
        "colab_type": "code",
        "outputId": "520338c4-d6c7-4a83-a608-7fbdb1caa0ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/reiinakano/SPIRAL-tensorflow.git\n",
        "!cd SPIRAL-tensorflow && git checkout reiinakano-patch-2  #reiinakano-patches\n",
        "!cd SPIRAL-tensorflow && git pull"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'SPIRAL-tensorflow'...\n",
            "remote: Enumerating objects: 8, done.\u001b[K\n",
            "remote: Counting objects: 100% (8/8), done.\u001b[K\n",
            "remote: Compressing objects: 100% (8/8), done.\u001b[K\n",
            "remote: Total 154 (delta 3), reused 0 (delta 0), pack-reused 146\u001b[K\n",
            "Receiving objects: 100% (154/154), 1.36 MiB | 3.61 MiB/s, done.\n",
            "Resolving deltas: 100% (75/75), done.\n",
            "Branch 'reiinakano-patch-2' set up to track remote branch 'reiinakano-patch-2' from 'origin'.\n",
            "Switched to a new branch 'reiinakano-patch-2'\n",
            "Already up to date.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "a2NYZTrugOeu",
        "colab_type": "code",
        "outputId": "322a159d-b86a-4c29-85f5-44d3459fffea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        }
      },
      "cell_type": "code",
      "source": [
        "! wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "! unzip ngrok-stable-linux-amd64.zip"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-04-13 10:46:48--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
            "Resolving bin.equinox.io (bin.equinox.io)... 52.73.94.166, 52.55.191.55, 52.7.169.168, ...\n",
            "Connecting to bin.equinox.io (bin.equinox.io)|52.73.94.166|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 14977695 (14M) [application/octet-stream]\n",
            "Saving to: ‘ngrok-stable-linux-amd64.zip’\n",
            "\n",
            "ngrok-stable-linux- 100%[===================>]  14.28M  14.6MB/s    in 1.0s    \n",
            "\n",
            "2019-04-13 10:46:49 (14.6 MB/s) - ‘ngrok-stable-linux-amd64.zip’ saved [14977695/14977695]\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "  inflating: ngrok                   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yQA6CkVzTAu4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Download the CelebA dataset\n",
        "\n",
        "We download the CelebA dataset from Kaggle. You will need to set your Kaggle API credentials via https://github.com/Kaggle/kaggle-api#api-credentials"
      ]
    },
    {
      "metadata": {
        "id": "KmSBAF-vTMDU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "923c58fd-1517-46ab-e806-fe62a630ef4b"
      },
      "cell_type": "code",
      "source": [
        "!pip install kaggle"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python2.7/dist-packages (1.5.3)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python2.7/dist-packages (from kaggle) (1.22)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python2.7/dist-packages (from kaggle) (1.11.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python2.7/dist-packages (from kaggle) (2019.3.9)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python2.7/dist-packages (from kaggle) (2.5.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python2.7/dist-packages (from kaggle) (2.18.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python2.7/dist-packages (from kaggle) (4.28.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python2.7/dist-packages (from kaggle) (3.0.2)\n",
            "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python2.7/dist-packages (from requests->kaggle) (2.6)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python2.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: text-unidecode==1.2 in /usr/local/lib/python2.7/dist-packages (from python-slugify->kaggle) (1.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0Kwi62fJTNHo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "\n",
        "# Make sure to upload your Kaggle key from somewhere. I saved mine in Drive\n",
        "#!cp \"/drive/My Drive/kaggle.json\" ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_QInVqJW0Nwr",
        "colab_type": "code",
        "outputId": "4b270356-67ed-4f05-88e7-f164631d941e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d jessicali9530/celeba-dataset -f img_align_celeba.zip\n",
        "!unzip -q img_align_celeba.zip"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading img_align_celeba.zip to /content\n",
            " 99% 1.33G/1.34G [00:18<00:00, 89.7MB/s]\n",
            "100% 1.34G/1.34G [00:18<00:00, 76.0MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_YFJNtlKvj4p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dZNVvr-OXNz8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Download pre-trained VAE and GAN Neural Painters\n",
        "\n",
        "I have prepared pre-trained VAE and GAN Neural Painters here but feel free to use your own if you wish.\n",
        "\n",
        "Place the VAE in tf_vae and the GAN in tf_gan3"
      ]
    },
    {
      "metadata": {
        "id": "Bgx8N4o6uXJE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir tf_vae\n",
        "!wget -O tf_vae/vae-300000.index 'https://docs.google.com/uc?export=download&id=1ulHdDxebH46m_0ZoLa2Wsz_6vStYqJQm'\n",
        "!wget -O tf_vae/vae-300000.meta 'https://docs.google.com/uc?export=download&id=1nHN_i7Ro9g0lP4y_YQCvIWrOVX1I3CJa'\n",
        "!wget -O tf_vae/vae-300000.data-00000-of-00001 'https://docs.google.com/uc?export=download&id=18rAJcUJwFJOAcjzsabtqK12udsHMZkVk'\n",
        "!wget -O tf_vae/checkpoint 'https://docs.google.com/uc?export=download&id=18U4qMNBdyvEk-Y-Mr3MNPEHSHxhcO9hn'\n",
        "\n",
        "!mkdir tf_gan3\n",
        "!wget -O tf_gan3/gan-571445.meta 'https://docs.google.com/uc?export=download&id=15kEG1Tiu2FUg5SILVt_9yOsSd3QHwVGA'\n",
        "!wget -O tf_gan3/gan-571445.index 'https://docs.google.com/uc?export=download&id=11uyFbQsRZoWa9Yq52AFXDXPjPQoGF_ER'\n",
        "!wget -O tf_gan3/gan-571445.data-00000-of-00001 'https://docs.google.com/uc?export=download&id=11cbvz-CH3KvfZEwNQ2OUujfbf6AKNoQa'\n",
        "!wget -O tf_gan3/checkpoint 'https://docs.google.com/uc?export=download&id=1A539u51t0L31Ab1M2uPUV2SsCFsNDQRo'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cf8wNMfICWqW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Imports\n",
        "\n",
        "If you encounter `SyntaxError: encoding problem: future_fstrings` then please \"Restart Runtime\" (NOT \"Reset all Runtimes\") and start from this point. I think this is because we are installing a library \"future-fstrings\" but it won't get loaded in until the runtime is reset :( If someone knows a fix please let me know."
      ]
    },
    {
      "metadata": {
        "id": "QYBRFuZqRqHL",
        "colab_type": "code",
        "outputId": "258b3166-f3c5-498a-c968-14c9dc69f879",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from pathlib import Path\n",
        "import json\n",
        "\n",
        "import tensorflow as tf\n",
        "import os\n",
        "import random\n",
        "import time\n",
        "from collections import namedtuple\n",
        "\n",
        "\n",
        "import sys\n",
        "sys.path.append('mypaint')\n",
        "sys.path.append('SPIRAL-tensorflow')\n",
        "\n",
        "from tqdm import tqdm\n",
        "import tensorflow as tf\n",
        "from PIL import Image, ImageDraw\n",
        "from collections import defaultdict\n",
        "\n",
        "from lib import surface, tiledsurface, brush\n",
        "import utils as ut\n",
        "from envs.mypaint_utils import *\n",
        "\n",
        "import moviepy.editor as mpy\n",
        "from IPython.display import display\n",
        "\n",
        "import tensorflow.contrib.layers as tcl\n",
        "\n",
        "import imageio\n",
        "\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Imageio: 'ffmpeg-linux64-v3.3.1' was not found on your computer; downloading it now.\n",
            "Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg-linux64-v3.3.1 (43.8 MB)\n",
            "Downloading: 8192/45929032 bytes (0.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b3375104/45929032 bytes (7.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b7086080/45929032 bytes (15.4%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b11018240/45929032 bytes (24.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b14753792/45929032 bytes (32.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b18612224/45929032 bytes (40.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b22454272/45929032 bytes (48.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b26312704/45929032 bytes (57.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b30007296/45929032 bytes (65.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b33931264/45929032 bytes (73.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b37814272/45929032 bytes (82.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b41623552/45929032 bytes (90.6%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b45555712/45929032 bytes (99.2%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b45929032/45929032 bytes (100.0%)\n",
            "  Done\n",
            "File saved as /root/.imageio/ffmpeg/ffmpeg-linux64-v3.3.1.\n",
            "1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CQmzry2j7SdZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "imageio.plugins.freeimage.download()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EbQ6yU1vEHZN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class args:\n",
        "  jump=True\n",
        "  curve=True\n",
        "  screen_size=64\n",
        "  location_size=32\n",
        "  color_channel=3\n",
        "  brush_path='SPIRAL-tensorflow/assets/brushes/dry_brush.myb'\n",
        "  train=True\n",
        "  data_dir=Path('data')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ivFxNXdqC7G9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# paint environment"
      ]
    },
    {
      "metadata": {
        "id": "vqG26OYpDHXe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class PaintMode:\n",
        "  STROKES_ONLY = 0\n",
        "  JUMP_STROKES = 1\n",
        "  CONNECTED_STROKES = 2\n",
        "\n",
        "class ColorEnv():\n",
        "    head = 0.25\n",
        "    tail = 0.75\n",
        "    \n",
        "    # all 0 to 1\n",
        "    actions_to_idx = {\n",
        "        'pressure': 0,\n",
        "        'size': 1,\n",
        "        'control_x': 2,\n",
        "        'control_y': 3,\n",
        "        'end_x': 4,\n",
        "        'end_y': 5,\n",
        "        'color_r': 6,\n",
        "        'color_g': 7,\n",
        "        'color_b': 8,\n",
        "        'start_x': 9,\n",
        "        'start_y': 10,\n",
        "        'entry_pressure': 11,\n",
        "    }\n",
        "\n",
        "    def __init__(self, args, paint_mode=PaintMode.JUMP_STROKES):\n",
        "        self.args = args\n",
        "        self.paint_mode = paint_mode\n",
        "\n",
        "        # screen\n",
        "        self.screen_size = args.screen_size\n",
        "        self.height, self.width = self.screen_size, self.screen_size\n",
        "        self.observation_shape = [\n",
        "                self.height, self.width, args.color_channel]\n",
        "\n",
        "        # location\n",
        "        self.location_size = args.location_size\n",
        "        self.location_shape = [self.location_size, self.location_size]\n",
        "        \n",
        "        self.prev_x, self.prev_y, self.prev_pressure = None, None, None\n",
        "    \n",
        "    @staticmethod\n",
        "    def pretty_print_action(ac):\n",
        "        for k, v in ColorEnv.actions_to_idx.items():\n",
        "            print(k, ac[v])\n",
        "    \n",
        "    def random_action(self):\n",
        "        return np.random.uniform(size=[len(self.actions_to_idx)])\n",
        "      \n",
        "    def reset(self):\n",
        "        self.intermediate_images = []\n",
        "        self.prev_x, self.prev_y, self.prev_pressure = None, None, None\n",
        "\n",
        "        self.s = tiledsurface.Surface()\n",
        "        self.s.flood_fill(0, 0, (255, 255, 255), (0, 0, 64, 64), 0, self.s)\n",
        "        self.s.begin_atomic()\n",
        "\n",
        "        with open(self.args.brush_path) as fp:\n",
        "            self.bi = brush.BrushInfo(fp.read())\n",
        "        self.b = brush.Brush(self.bi)\n",
        "\n",
        "    def draw(self, ac, s=None, dtime=1):\n",
        "        # Just added this\n",
        "        if self.paint_mode == PaintMode.STROKES_ONLY:\n",
        "          self.s.clear()\n",
        "          self.s.flood_fill(0, 0, (255, 255, 255), (0, 0, 64, 64), 0, self.s)\n",
        "          self.s.end_atomic()\n",
        "          self.s.begin_atomic()\n",
        "        \n",
        "        if s is None:\n",
        "            s = self.s\n",
        "\n",
        "        s_x, s_y = ac[self.actions_to_idx['start_x']]*64, ac[self.actions_to_idx['start_y']]*64  \n",
        "        e_x, e_y = ac[self.actions_to_idx['end_x']]*64, ac[self.actions_to_idx['end_y']]*64\n",
        "        c_x, c_y = ac[self.actions_to_idx['control_x']]*64, ac[self.actions_to_idx['control_y']]*64\n",
        "        color = (\n",
        "            ac[self.actions_to_idx['color_r']],\n",
        "            ac[self.actions_to_idx['color_g']],\n",
        "            ac[self.actions_to_idx['color_b']],\n",
        "        )\n",
        "        pressure = ac[self.actions_to_idx['pressure']]*0.8\n",
        "        entry_pressure = ac[self.actions_to_idx['entry_pressure']]*0.8\n",
        "        size = ac[self.actions_to_idx['size']] * 2.\n",
        "        \n",
        "        if self.paint_mode == PaintMode.CONNECTED_STROKES:\n",
        "            if self.prev_x is not None:\n",
        "                s_x, s_y, entry_pressure = self.prev_x, self.prev_y, self.prev_pressure\n",
        "            self.prev_x, self.prev_y, self.prev_pressure = e_x, e_y, pressure\n",
        "\n",
        "        self.b.brushinfo.set_color_rgb(color)\n",
        "        \n",
        "        self.b.brushinfo.set_base_value('radius_logarithmic', size)\n",
        "\n",
        "        # Move brush to starting point without leaving it on the canvas.\n",
        "        self._stroke_to(s_x, s_y, 0)\n",
        "\n",
        "        self._draw(s_x, s_y, e_x, e_y, c_x, c_y, entry_pressure, pressure, size, color, dtime)\n",
        "\n",
        "    def _draw(self, s_x, s_y, e_x, e_y, c_x, c_y,\n",
        "              entry_pressure, pressure, size, color, dtime):\n",
        "\n",
        "        # if straight line or jump\n",
        "        if pressure == 0:\n",
        "            self.b.stroke_to(\n",
        "                    self.s.backend, e_x, e_y, pressure, 0, 0, dtime)\n",
        "        else:\n",
        "            self.curve(c_x, c_y, s_x, s_y, e_x, e_y, entry_pressure, pressure)\n",
        "            \n",
        "        # Relieve brush pressure for next jump\n",
        "        self._stroke_to(e_x, e_y, 0)\n",
        "\n",
        "        self.s.end_atomic()\n",
        "        self.s.begin_atomic()\n",
        "\n",
        "    # sx, sy = starting point\n",
        "    # ex, ey = end point\n",
        "    # kx, ky = curve point from last line\n",
        "    # lx, ly = last point from InteractionMode update\n",
        "    def curve(self, cx, cy, sx, sy, ex, ey, entry_pressure, pressure):\n",
        "        #entry_p, midpoint_p, junk, prange2, head, tail\n",
        "        entry_p, midpoint_p, prange1, prange2, h, t = \\\n",
        "                self._line_settings(entry_pressure, pressure)\n",
        "\n",
        "        points_in_curve = 100\n",
        "        mx, my = midpoint(sx, sy, ex, ey)\n",
        "        length, nx, ny = length_and_normal(mx, my, cx, cy)\n",
        "        cx, cy = multiply_add(mx, my, nx, ny, length*2)\n",
        "        x1, y1 = difference(sx, sy, cx, cy)\n",
        "        x2, y2 = difference(cx, cy, ex, ey)\n",
        "        head = points_in_curve * h\n",
        "        head_range = int(head)+1\n",
        "        tail = points_in_curve * t\n",
        "        tail_range = int(tail)+1\n",
        "        tail_length = points_in_curve - tail\n",
        "\n",
        "        # Beginning\n",
        "        px, py = point_on_curve_1(1, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "        length, nx, ny = length_and_normal(sx, sy, px, py)\n",
        "        bx, by = multiply_add(sx, sy, nx, ny, 0.25)\n",
        "        self._stroke_to(bx, by, entry_p)\n",
        "        pressure = abs(1/head * prange1 + entry_p)\n",
        "        self._stroke_to(px, py, pressure)\n",
        "\n",
        "        for i in xrange(2, head_range):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            pressure = abs(i/head * prange1 + entry_p)\n",
        "            self._stroke_to(px, py, pressure)\n",
        "\n",
        "        # Middle\n",
        "        for i in xrange(head_range, tail_range):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            self._stroke_to(px, py, midpoint_p)\n",
        "\n",
        "        # End\n",
        "        for i in xrange(tail_range, points_in_curve+1):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            pressure = abs((i-tail)/tail_length * prange2 + midpoint_p)\n",
        "            self._stroke_to(px, py, pressure)\n",
        "\n",
        "        return pressure\n",
        "\n",
        "    def _stroke_to(self, x, y, pressure, duration=0.1):\n",
        "        self.b.stroke_to(\n",
        "                self.s.backend,\n",
        "                x, y,\n",
        "                pressure,\n",
        "                0.0, 0.0,\n",
        "                duration)\n",
        "        self.s.end_atomic()\n",
        "        self.s.begin_atomic()\n",
        "        self.intermediate_images.append(self.image)\n",
        "\n",
        "    def save_image(self, path=\"test.png\"):\n",
        "        Image.fromarray(self.image.astype(np.uint8).squeeze()).save(path)\n",
        "        #self.s.save_as_png(path, alpha=False)\n",
        "\n",
        "    @property\n",
        "    def image(self):\n",
        "        rect = [0, 0, self.height, self.width]\n",
        "        scanline_strips = \\\n",
        "                surface.scanline_strips_iter(self.s, rect)\n",
        "        return next(scanline_strips)\n",
        "\n",
        "    def _line_settings(self, entry_pressure, pressure):\n",
        "        p1 = entry_pressure\n",
        "        p2 = (entry_pressure + pressure) / 2\n",
        "        p3 = pressure\n",
        "        if self.head == 0.0001:\n",
        "            p1 = p2\n",
        "        prange1 = p2 - p1\n",
        "        prange2 = p3 - p2\n",
        "        return p1, p2, prange1, prange2, self.head, self.tail\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "r2sqbc-4D_pk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# celeba environment"
      ]
    },
    {
      "metadata": {
        "id": "zXATLvvnmGcW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class CelebADispenser():\n",
        "\n",
        "    def __init__(self, data_dir=Path('data'), screen_size=64):\n",
        "        self.data_dir = data_dir\n",
        "        self.height, self.width = screen_size, screen_size\n",
        "        self.prepare_data()\n",
        "\n",
        "    def get_random_target(self, num=1, squeeze=False, train=True):\n",
        "        data_length = self.real_data.shape[0]\n",
        "        train_len = int(data_length*0.9)\n",
        "        if train:\n",
        "          a = train_len\n",
        "        else:\n",
        "          a = np.arange(train_len, data_length)\n",
        "        random_idxes = np.random.choice(a, num, replace=False)\n",
        "        random_image = self.real_data[random_idxes]\n",
        "        if squeeze:\n",
        "            random_image = np.squeeze(random_image, 0)\n",
        "        return random_image\n",
        "      \n",
        "    def prepare_data(self):\n",
        "        ut.io.makedirs(self.data_dir)\n",
        "        \n",
        "        omniglot_image_files = tf.gfile.Glob('img_align_celeba/*')\n",
        "\n",
        "        # ground truth omniglot data\n",
        "        mnist_dir = self.data_dir / 'celeba'\n",
        "        try:\n",
        "          os.makedirs(str(mnist_dir))\n",
        "        except OSError:\n",
        "          pass\n",
        "        pkl_path = mnist_dir / 'celeba_dict.pkl'\n",
        "        if pkl_path.exists():\n",
        "            self.real_data = np.load(pkl_path)\n",
        "        else:\n",
        "            omniglot_list = []\n",
        "            #iterator = tqdm(omniglot_image_files, desc=\"Processing\")\n",
        "            for idx, img in enumerate(omniglot_image_files):\n",
        "              if idx % 10000 == 0: print(idx)\n",
        "              img = ut.io.imread(img)\n",
        "              img = ut.io.imresize(\n",
        "                                img, [self.height, self.width],\n",
        "                                interp='cubic')\n",
        "              omniglot_list.append(img)\n",
        "                          \n",
        "            self.real_data = np.concatenate(omniglot_list).reshape(-1, 64, 64, 3)\n",
        "            with open(str(pkl_path), 'w') as f:\n",
        "              np.save(f, self.real_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kLMmcTHo7YT4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Sanity check\n",
        "#celeba_dispenser = CelebADispenser()\n",
        "#celeba_dispenser.get_random_target().shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "l6I5xeZ6GUC-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jSF1EvQvGUUj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# VAE Painter\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "axiRNmrtlcAK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class ConvVAE(object):\n",
        "  def __init__(self, z_size=64, batch_size=100, learning_rate=0.0001, kl_tolerance=0.5, is_training=True, reuse=False, gpu_mode=True, graph=None):\n",
        "    self.z_size = z_size\n",
        "    self.batch_size = batch_size\n",
        "    self.learning_rate = learning_rate\n",
        "    self.is_training = is_training\n",
        "    self.kl_tolerance = kl_tolerance\n",
        "    self.reuse = reuse\n",
        "    # Is it okay to comment this out? with tf.variable_scope('conv_vae', reuse=self.reuse):\n",
        "    if not gpu_mode:\n",
        "      with tf.device('/cpu:0'):\n",
        "        tf.logging.info('conv_vae using cpu.')\n",
        "        self._build_graph(graph)\n",
        "    else:\n",
        "      tf.logging.info('conv_vae using gpu.')\n",
        "      self._build_graph(graph)\n",
        "    self._init_session()\n",
        "  \n",
        "  def build_decoder(self, z, reuse=False):\n",
        "    with tf.variable_scope('decoder', reuse=reuse):\n",
        "      h = tf.layers.dense(z, 4*256, name=\"fc\")\n",
        "      h = tf.reshape(h, [-1, 1, 1, 4*256])\n",
        "      h = tf.layers.conv2d_transpose(h, 128, 5, strides=2, activation=tf.nn.relu, name=\"deconv1\")\n",
        "      h = tf.layers.conv2d_transpose(h, 64, 5, strides=2, activation=tf.nn.relu, name=\"deconv2\")\n",
        "      h = tf.layers.conv2d_transpose(h, 32, 6, strides=2, activation=tf.nn.relu, name=\"deconv3\")\n",
        "      return tf.layers.conv2d_transpose(h, 3, 6, strides=2, activation=tf.nn.sigmoid, name=\"deconv4\")\n",
        "  \n",
        "  def build_predictor(self, actions, reuse=False, is_training=False):\n",
        "    with tf.variable_scope('predictor', reuse=reuse):\n",
        "      h = tf.layers.dense(actions, 256, activation=tf.nn.leaky_relu, name=\"fc1\")\n",
        "      h = tf.layers.batch_normalization(h, training=is_training, name=\"bn1\")\n",
        "      h = tf.layers.dense(h, 64, activation=tf.nn.leaky_relu, name=\"fc2\")\n",
        "      h = tf.layers.batch_normalization(h, training=is_training, name=\"bn2\")\n",
        "      h = tf.layers.dense(h, 64, activation=tf.nn.leaky_relu, name=\"fc3\")\n",
        "      h = tf.layers.batch_normalization(h, training=is_training, name=\"bn3\")\n",
        "      return tf.layers.dense(h, self.z_size, name='fc4')\n",
        "  \n",
        "  def _build_graph(self, graph):\n",
        "    if graph is None:\n",
        "      self.g = tf.Graph()\n",
        "    else:\n",
        "      self.g = graph\n",
        "    with self.g.as_default(), tf.variable_scope('conv_vae', reuse=self.reuse):\n",
        "\n",
        "      #### autoencoding part\n",
        "      self.x = tf.placeholder(tf.float32, shape=[None, 64, 64, 3])\n",
        "      \n",
        "      # Encoder\n",
        "      h = tf.layers.conv2d(self.x, 32, 4, strides=2, activation=tf.nn.relu, name=\"enc_conv1\")\n",
        "      h = tf.layers.conv2d(h, 64, 4, strides=2, activation=tf.nn.relu, name=\"enc_conv2\")\n",
        "      h = tf.layers.conv2d(h, 128, 4, strides=2, activation=tf.nn.relu, name=\"enc_conv3\")\n",
        "      h = tf.layers.conv2d(h, 256, 4, strides=2, activation=tf.nn.relu, name=\"enc_conv4\")\n",
        "      h = tf.reshape(h, [-1, 2*2*256])\n",
        "\n",
        "      # VAE\n",
        "      self.mu = tf.layers.dense(h, self.z_size, name=\"enc_fc_mu\")\n",
        "      self.logvar = tf.layers.dense(h, self.z_size, name=\"enc_fc_log_var\")\n",
        "      self.sigma = tf.exp(self.logvar / 2.0)\n",
        "      #self.epsilon = tf.random_normal([self.batch_size, self.z_size])\n",
        "      self.epsilon = tf.random_normal([tf.shape(self.sigma)[0], self.z_size])\n",
        "      self.z = self.mu + self.sigma * self.epsilon\n",
        "\n",
        "      # Decoder\n",
        "      self.y = self.build_decoder(self.z)\n",
        "      \n",
        "      #### predicting part\n",
        "      self.actions = tf.placeholder(tf.float32, shape=[None, 12])\n",
        "      self.predicted_z = self.build_predictor(self.actions, is_training=self.is_training)\n",
        "      self.predicted_y = self.build_decoder(self.predicted_z, reuse=True)\n",
        "      \n",
        "      # train ops\n",
        "      if self.is_training:\n",
        "        self.global_step = tf.Variable(0, name='global_step', trainable=False)\n",
        "        self.uneven_multiplier = tf.placeholder_with_default(1.0, [])\n",
        "        summ_uneven_mult = tf.summary.scalar('uneven_multiplier', self.uneven_multiplier)\n",
        "\n",
        "        eps = 1e-6 # avoid taking log of zero\n",
        "        \n",
        "        mask = tf.reduce_mean(\n",
        "          self.x,\n",
        "          reduction_indices = [3]\n",
        "        )\n",
        "        stroke_whitespace = tf.equal(mask, 1.0)\n",
        "        mask = tf.where(stroke_whitespace, tf.ones(tf.shape(mask)), self.uneven_multiplier*tf.ones(tf.shape(mask)))\n",
        "        mask = tf.reshape(mask, [-1, 64, 64, 1])\n",
        "        mask = tf.tile(mask, [1, 1, 1, 3])\n",
        "        summ_mask = tf.summary.image('mask', mask, max_outputs=3)\n",
        "        print(mask.get_shape())\n",
        "        \n",
        "        # reconstruction loss\n",
        "        self.r_loss = tf.reduce_sum(\n",
        "          (tf.square(self.x - self.y))*mask,\n",
        "          reduction_indices = [1,2,3]\n",
        "        )\n",
        "        self.r_loss = tf.reduce_mean(self.r_loss)\n",
        "\n",
        "        # augmented kl loss per dim\n",
        "        self.kl_loss = - 0.5 * tf.reduce_sum(\n",
        "          (1 + self.logvar - tf.square(self.mu) - tf.exp(self.logvar)),\n",
        "          reduction_indices = 1\n",
        "        )\n",
        "        self.kl_loss = tf.maximum(self.kl_loss, self.kl_tolerance * self.z_size)\n",
        "        self.kl_loss = tf.reduce_mean(self.kl_loss)\n",
        "        \n",
        "        summ_recon_loss = tf.summary.scalar('recon_loss', self.r_loss)\n",
        "        summ_kl_loss = tf.summary.scalar('kl_loss', self.kl_loss)\n",
        "        self.loss = self.r_loss + self.kl_loss\n",
        "        \n",
        "        # training the vae\n",
        "        self.lr = tf.Variable(self.learning_rate, trainable=False)\n",
        "        summ_lr = tf.summary.scalar('lr', self.lr)\n",
        "        self.optimizer = tf.train.AdamOptimizer(self.lr)\n",
        "        grads = self.optimizer.compute_gradients(\n",
        "            self.loss, \n",
        "            tf.global_variables('conv_vae/enc_*')+tf.global_variables('conv_vae/decoder*')) # can potentially clip gradients here.\n",
        "\n",
        "        self.train_op = self.optimizer.apply_gradients(\n",
        "          grads, global_step=self.global_step, name='train_step')\n",
        "        summ_loss = tf.summary.scalar('loss', self.loss)\n",
        "        \n",
        "        # training the predictor\n",
        "        self.predictor_loss = tf.reduce_mean(tf.square(self.predicted_z - self.z))\n",
        "        self.optimizer2 = tf.train.AdamOptimizer(self.lr)\n",
        "        grads2 = self.optimizer2.compute_gradients(self.predictor_loss, tf.global_variables('conv_vae/predictor*'))\n",
        "        with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
        "          self.train_op2 = self.optimizer2.apply_gradients(\n",
        "            grads2, global_step=self.global_step, name='train_step2')\n",
        "        summ_predictor_loss = tf.summary.scalar('predictor_loss', self.predictor_loss)\n",
        "      \n",
        "        #summary ops\n",
        "        summ_inp_img = tf.summary.image('inp_img', self.x, max_outputs=3)\n",
        "        summ_output_img = tf.summary.image('output_img', self.y, max_outputs=3)\n",
        "        summ_predicted_output_img = tf.summary.image('predicted_output_img', self.predicted_y, max_outputs=3)\n",
        "        self.summary_op = tf.summary.merge([\n",
        "            summ_inp_img, summ_output_img, summ_loss, summ_kl_loss, summ_recon_loss,\n",
        "            summ_mask, summ_uneven_mult, summ_lr\n",
        "        ])\n",
        "        self.summary_op_2 = tf.summary.merge([\n",
        "            summ_inp_img, summ_predictor_loss, summ_output_img, summ_predicted_output_img, summ_lr\n",
        "        ])\n",
        "        \n",
        "\n",
        "      # initialize vars\n",
        "      self.init = tf.global_variables_initializer()\n",
        "  \n",
        "  def generate_stroke_graph(self, actions):\n",
        "    with tf.variable_scope('conv_vae', reuse=True):\n",
        "      with self.g.as_default():\n",
        "        # Encoder?\n",
        "        z = self.build_predictor(actions, reuse=True, is_training=False)\n",
        "\n",
        "        # Decoder\n",
        "        return self.build_decoder(z, reuse=True)\n",
        "\n",
        "  def _init_session(self):\n",
        "    \"\"\"Launch TensorFlow session and initialize variables\"\"\"\n",
        "    self.sess = tf.Session(graph=self.g)\n",
        "    self.sess.run(self.init)\n",
        "  def close_sess(self):\n",
        "    \"\"\" Close TensorFlow session \"\"\"\n",
        "    self.sess.close()\n",
        "  def save_model(self, model_save_path='tf_vae'):\n",
        "    sess = self.sess\n",
        "    step = sess.run(self.global_step)\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    checkpoint_path = os.path.join(model_save_path, 'vae')\n",
        "    tf.logging.info('saving model %s.', checkpoint_path)\n",
        "    saver.save(sess, checkpoint_path, step) # just keep one\n",
        "  def load_checkpoint(self, checkpoint_path='tf_vae', actual_path=None):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    ckpt = tf.train.get_checkpoint_state(checkpoint_path)\n",
        "    if actual_path is None:\n",
        "      actual_path = ckpt.model_checkpoint_path\n",
        "    print('loading model', actual_path)\n",
        "    tf.logging.info('Loading model %s.', actual_path)\n",
        "    saver.restore(sess, actual_path)\n",
        "  def load_only_vae_checkpoint(self, checkpoint_path='tf_vae', actual_path=None):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      to_save = tf.global_variables('conv_vae/enc_*')+tf.global_variables('conv_vae/decoder*')+tf.global_variables('conv_vae/conv_vae/enc_*')+tf.global_variables('conv_vae/conv_vae/decoder*')+tf.global_variables('conv_vae/global_step')\n",
        "      print(to_save)\n",
        "      saver = tf.train.Saver(to_save)\n",
        "    ckpt = tf.train.get_checkpoint_state(checkpoint_path)\n",
        "    if actual_path is None:\n",
        "      actual_path = ckpt.model_checkpoint_path\n",
        "    print('loading model', actual_path)\n",
        "    tf.logging.info('Loading model %s.', actual_path)\n",
        "    saver.restore(sess, actual_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lfMJ6xCEoVSu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-q7nTIXRmFBJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# GAN Painter"
      ]
    },
    {
      "metadata": {
        "id": "qS1xkGT3mGDm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def leaky_relu(x, alpha=0.2):\n",
        "    return tf.maximum(tf.minimum(0.0, alpha * x), x)\n",
        "\n",
        "def leaky_relu_batch_norm(x, alpha=0.2):\n",
        "    return leaky_relu(tf.contrib.layers.batch_norm(x, updates_collections=None), alpha)\n",
        "\n",
        "def relu_batch_norm(x):\n",
        "    return tf.nn.relu(tf.contrib.layers.batch_norm(x, updates_collections=None))\n",
        "  \n",
        "class DiscriminatorConditionalWM(object):\n",
        "    def __init__(self, divisor=1):\n",
        "        \"\"\"\n",
        "        make the network smaller by divisor times\n",
        "        \"\"\"\n",
        "        self.x_dim = 64 * 64 * 3\n",
        "        self.name = 'lsun/dcgan/d_net'\n",
        "        self.divisor=divisor\n",
        "        \n",
        "    def __call__(self, x, conditions, reuse=True):\n",
        "        with tf.variable_scope(self.name) as vs:\n",
        "            if reuse:\n",
        "                vs.reuse_variables()\n",
        "            bs = tf.shape(x)[0]\n",
        "            x = tf.reshape(x, [bs, 64, 64, 3])\n",
        "            conditions = tf.reshape(conditions, [bs, 12])\n",
        "            conditions = tf.contrib.layers.fully_connected(conditions, 64/self.divisor)\n",
        "            conditions = tf.reshape(conditions, [bs, 1, 1, 64/self.divisor])\n",
        "            conv1 = tcl.conv2d(\n",
        "                x, 64/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu\n",
        "            )\n",
        "            conv1 = conv1 + conditions\n",
        "            conv2 = tcl.conv2d(\n",
        "                conv1, 128/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv3 = tcl.conv2d(\n",
        "                conv2, 256/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv4 = tcl.conv2d(\n",
        "                conv3, 512/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv4 = tcl.flatten(conv4)\n",
        "            fc = tcl.fully_connected(conv4, 1, activation_fn=tf.identity)\n",
        "            return fc\n",
        "\n",
        "    @property\n",
        "    def vars(self):\n",
        "        return [var for var in tf.global_variables() if self.name in var.name]\n",
        "\n",
        "class GeneratorConditional(object):\n",
        "    def __init__(self, divisor=1, add_noise=False):\n",
        "        self.x_dim = 64 * 64 * 3\n",
        "        self.divisor=divisor\n",
        "        self.name = 'lsun/dcgan/g_net'\n",
        "        self.add_noise = add_noise\n",
        "\n",
        "    def __call__(self, conditions, is_training):\n",
        "        with tf.contrib.framework.arg_scope([tcl.batch_norm], \n",
        "                                            is_training=is_training):\n",
        "          with tf.variable_scope(self.name) as vs:\n",
        "              bs = tf.shape(conditions)[0]\n",
        "              if self.add_noise:\n",
        "                conditions = tf.concat([conditions, tf.random.uniform([bs, 10])], axis=1)\n",
        "              fc = tcl.fully_connected(conditions, 4 * 4 * 1024/self.divisor, activation_fn=tf.identity)\n",
        "              conv1 = tf.reshape(fc, tf.stack([bs, 4, 4, 1024/self.divisor]))\n",
        "              conv1 = relu_batch_norm(conv1)\n",
        "              conv2 = tcl.conv2d_transpose(\n",
        "                  conv1, 512/self.divisor, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=relu_batch_norm\n",
        "              )\n",
        "              conv3 = tcl.conv2d_transpose(\n",
        "                  conv2, 256/self.divisor, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=relu_batch_norm\n",
        "              )\n",
        "              conv4 = tcl.conv2d_transpose(\n",
        "                  conv3, 128/self.divisor, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=relu_batch_norm\n",
        "              )\n",
        "              conv5 = tcl.conv2d_transpose(\n",
        "                  conv4, 3, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=tf.sigmoid)\n",
        "              return conv5\n",
        "\n",
        "    @property\n",
        "    def vars(self):\n",
        "        return [var for var in tf.global_variables() if self.name in var.name]\n",
        "      \n",
        "class ConvGAN(object):\n",
        "  def __init__(self, learning_rate=0.0001, is_training=True, reuse=False, gpu_mode=True, data_glob='data/episodes_*.npz', divisor=1, add_noise=False, graph=None):\n",
        "    self.learning_rate = learning_rate\n",
        "    self.is_training = is_training\n",
        "    self.reuse = reuse\n",
        "    self.g_net = GeneratorConditional(divisor=divisor, add_noise=add_noise)\n",
        "    self.d_net = DiscriminatorConditionalWM(divisor=divisor)\n",
        "    \n",
        "    if self.is_training:\n",
        "      self.episode_files = tf.gfile.Glob(data_glob)\n",
        "      np.random.shuffle(self.episode_files)\n",
        "      self.ep_file_ctr = 0\n",
        "      self.current_file_idx = 0\n",
        "      loaded = np.load(self.episode_files[0])\n",
        "      self.loaded_strokes = loaded['strokes']\n",
        "      self.loaded_actions = loaded['actions']\n",
        "      self.len_loaded = len(self.loaded_strokes)\n",
        "    \n",
        "    if not gpu_mode:\n",
        "      with tf.device('/cpu:0'):\n",
        "        tf.logging.info('conv_gan using cpu.')\n",
        "        self._build_training_or_generator_graph(graph)\n",
        "    else:\n",
        "      tf.logging.info('conv_gan using gpu.')\n",
        "      self._build_training_or_generator_graph(graph)\n",
        "    self._init_session()\n",
        "   \n",
        "  def _build_training_or_generator_graph(self, graph):\n",
        "    if self.is_training:\n",
        "      self._build_graph(graph)\n",
        "    else:\n",
        "      self._build_generator_graph(graph)\n",
        "  \n",
        "  def _build_graph(self, graph):\n",
        "    if graph is None:\n",
        "      self.g = tf.Graph()\n",
        "    else:\n",
        "      self.g = graph\n",
        "    with self.g.as_default(), tf.variable_scope('conv_gan', reuse=self.reuse):\n",
        "\n",
        "      self.x = tf.placeholder(tf.float32, shape=[None, 64, 64, 3])\n",
        "      self.actions = tf.placeholder(tf.float32, shape=[None, 12])\n",
        "      \n",
        "      self.y = self.g_net(self.actions, is_training=self.is_training)\n",
        "      \n",
        "      real_score = tf.reduce_mean(self.d_net(self.x, self.actions, reuse=False))\n",
        "      generated_score = tf.reduce_mean(self.d_net(self.y, self.actions))\n",
        "      \n",
        "      real_score_summ = tf.summary.scalar('real_score', real_score)\n",
        "      g_loss_summ = tf.summary.scalar('generated_score/g_loss', generated_score)\n",
        "      \n",
        "      reconstruction_loss = tf.reduce_mean(tf.square(self.y-self.x))\n",
        "      recon_loss_summ = tf.summary.scalar('recon_loss', reconstruction_loss)\n",
        "      \n",
        "      # actual reconstruction loss used for training\n",
        "      self.uneven_multiplier = tf.placeholder_with_default(1.0, [])\n",
        "      u_m_summ = tf.summary.scalar('uneven_multiplier', self.uneven_multiplier)\n",
        "      mask = tf.reduce_mean(\n",
        "        self.x,\n",
        "        reduction_indices = [3]\n",
        "      )\n",
        "      stroke_whitespace = tf.equal(mask, 1.0)\n",
        "      mask = tf.where(stroke_whitespace, tf.ones(tf.shape(mask)), self.uneven_multiplier*tf.ones(tf.shape(mask)))\n",
        "      mask = tf.reshape(mask, [-1, 64, 64, 1])\n",
        "      mask = tf.tile(mask, [1, 1, 1, 3])\n",
        "      tf.summary.image('mask', mask, max_outputs=3)\n",
        "      print(mask.get_shape())\n",
        "      self.r_loss = 10*tf.reduce_mean((tf.square(self.x - self.y))*mask)\n",
        "      actual_recon_loss_summ = tf.summary.scalar('recon_loss_actual', self.r_loss)\n",
        "      # /actual reconstruction loss used for training\n",
        "      \n",
        "      self.g_loss = generated_score + self.r_loss\n",
        "      self.d_loss = real_score - generated_score\n",
        "      \n",
        "      epsilon = tf.random_uniform([], 0.0, 1.0)\n",
        "      x_hat = epsilon * self.x + (1 - epsilon) * self.y\n",
        "      d_hat = self.d_net(x_hat, self.actions)\n",
        "      \n",
        "      ddx = tf.gradients(d_hat, x_hat)[0]\n",
        "      print(ddx.get_shape().as_list())\n",
        "\n",
        "      ddx = tf.sqrt(tf.reduce_sum(tf.square(ddx), axis=(1, 2, 3)))\n",
        "      ddx = tf.reduce_mean(tf.square(ddx - 1.0) * 10.0)\n",
        "      \n",
        "      self.d_loss = self.d_loss + ddx\n",
        "      \n",
        "      gradient_penalty_summ = tf.summary.scalar('gradient_penalty', ddx)\n",
        "      d_loss_summ = tf.summary.scalar('actual_loss', self.d_loss)\n",
        "      \n",
        "      # train ops\n",
        "      if self.is_training:\n",
        "        self.global_step = tf.Variable(0, name='global_step', trainable=False)\n",
        "        self.inc_global_step = tf.assign(self.global_step, self.global_step+1)\n",
        "\n",
        "        self.d_adam, self.g_adam = None, None\n",
        "        with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
        "            self.d_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9)\\\n",
        "                .minimize(self.d_loss, var_list=self.d_net.vars)\n",
        "            self.g_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9)\\\n",
        "                .minimize(self.g_loss, var_list=self.g_net.vars)\n",
        "\n",
        "      # initialize vars\n",
        "      self.init = tf.global_variables_initializer()\n",
        "      \n",
        "      #summary ops\n",
        "      tf.summary.image('inp_img', self.x, max_outputs=3)\n",
        "      tf.summary.image('output_img', self.y, max_outputs=3)\n",
        "      self.summary_op = tf.summary.merge_all()\n",
        "      \n",
        "  def _build_generator_graph(self, graph):\n",
        "    if graph is None:\n",
        "      self.g = tf.Graph()\n",
        "    else:\n",
        "      self.g = graph\n",
        "      \n",
        "    with self.g.as_default(), tf.variable_scope('conv_gan', reuse=self.reuse):\n",
        "      self.actions = tf.placeholder(tf.float32, shape=[None, 12])\n",
        "      self.y = self.g_net(self.actions, is_training=self.is_training)\n",
        "      self.init = tf.global_variables_initializer()\n",
        "  \n",
        "  def generate_stroke_graph(self, actions):\n",
        "    with tf.variable_scope('conv_gan', reuse=True):\n",
        "      with self.g.as_default():\n",
        "        return self.g_net(actions, is_training=False)\n",
        "      \n",
        "  def get_random_batch(self, batch_size):\n",
        "    while self.len_loaded - self.current_file_idx < batch_size:\n",
        "      self.ep_file_ctr = (self.ep_file_ctr + 1) % len(self.episode_files)\n",
        "      self.current_file_idx = 0\n",
        "      print('loading new file', self.episode_files[self.ep_file_ctr])\n",
        "      loaded = np.load(self.episode_files[self.ep_file_ctr])\n",
        "      self.loaded_strokes = loaded['strokes']\n",
        "      self.loaded_actions = loaded['actions']\n",
        "      self.len_loaded = len(self.loaded_strokes)\n",
        "      rand_idx = np.random.permutation(self.len_loaded)\n",
        "      self.loaded_strokes = self.loaded_strokes[rand_idx]\n",
        "      self.loaded_actions = self.loaded_actions[rand_idx]\n",
        "    \n",
        "    strokes = self.loaded_strokes[self.current_file_idx:self.current_file_idx+batch_size]\n",
        "    strokes = strokes.astype(np.float)/255.0\n",
        "    actions = self.loaded_actions[self.current_file_idx:self.current_file_idx+batch_size]\n",
        "    \n",
        "    self.current_file_idx += batch_size\n",
        "    \n",
        "    return strokes, actions\n",
        "      \n",
        "  def train(self, batch_size=64, num_batches=1000000):\n",
        "    train_writer = tf.summary.FileWriter('logdir', self.g)\n",
        "    start_time = time.time()\n",
        "    uneven_multiplier = 10.\n",
        "    for t in range(num_batches):\n",
        "        d_iters = 5\n",
        "\n",
        "        for _ in range(0, d_iters):\n",
        "            strokes, actions = self.get_random_batch(batch_size)\n",
        "            _, summ = self.sess.run((self.d_adam, self.summary_op), \n",
        "                                    feed_dict={self.x: strokes, self.actions: actions})\n",
        "\n",
        "        strokes, actions = self.get_random_batch(batch_size)\n",
        "        _, summ, _, step = self.sess.run((self.g_adam, self.summary_op, self.inc_global_step, self.global_step), \n",
        "                                      feed_dict={self.x: strokes, \n",
        "                                                 self.actions: actions, \n",
        "                                                 self.uneven_multiplier: uneven_multiplier})\n",
        "        train_writer.add_summary(summ, step)\n",
        "        \n",
        "        if step > 295044:\n",
        "          uneven_multiplier = 1.\n",
        "\n",
        "        if step % 20 == 0:\n",
        "            strokes, actions = self.get_random_batch(batch_size)\n",
        "            d_loss = self.sess.run(\n",
        "                self.d_loss, feed_dict={self.x: strokes, self.actions: actions}\n",
        "            )\n",
        "            g_loss = self.sess.run(\n",
        "                self.g_loss, feed_dict={self.x: strokes, self.actions: actions}\n",
        "            )\n",
        "            print('Iter [%8d] Time [%5.4f] d_loss [%.4f] g_loss [%.4f]' %\n",
        "                    (step, time.time() - start_time, d_loss, g_loss))\n",
        "            \n",
        "        if step % 2000 == 0:\n",
        "            self.save_model(\"tf_gan3\")\n",
        "\n",
        "  def _init_session(self):\n",
        "    \"\"\"Launch TensorFlow session and initialize variables\"\"\"\n",
        "    self.sess = tf.Session(graph=self.g)\n",
        "    self.sess.run(self.init)\n",
        "  def close_sess(self):\n",
        "    \"\"\" Close TensorFlow session \"\"\"\n",
        "    self.sess.close()\n",
        "  def save_model(self, model_save_path='tf_gan3'):\n",
        "    sess = self.sess\n",
        "    step = sess.run(self.global_step)\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    checkpoint_path = os.path.join(model_save_path, 'gan')\n",
        "    tf.logging.info('saving model %s.', checkpoint_path)\n",
        "    saver.save(sess, checkpoint_path, step) # just keep one\n",
        "  def load_checkpoint(self, checkpoint_path='tf_gan3', actual_path=None):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    ckpt = tf.train.get_checkpoint_state(checkpoint_path)\n",
        "    if actual_path is None:\n",
        "      actual_path = ckpt.model_checkpoint_path\n",
        "    print('loading model', actual_path)\n",
        "    tf.logging.info('Loading model %s.', actual_path)\n",
        "    saver.restore(sess, actual_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Gm--vvK2HC1P",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Adversarial Training graph"
      ]
    },
    {
      "metadata": {
        "id": "VZF5_lQat8oC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def leaky_relu(x, alpha=0.2):\n",
        "    return tf.maximum(tf.minimum(0.0, alpha * x), x)\n",
        "\n",
        "def leaky_relu_batch_norm(x, alpha=0.2):\n",
        "    return leaky_relu(tf.contrib.layers.batch_norm(x, updates_collections=None), alpha)\n",
        "\n",
        "def relu_batch_norm(x):\n",
        "    return tf.nn.relu(tf.contrib.layers.batch_norm(x, updates_collections=None))\n",
        "\n",
        "def res_block(x, channel, size, name):\n",
        "    with tf.variable_scope(name):\n",
        "        enc_x = tf.contrib.layers.conv2d(\n",
        "                x, channel, size,\n",
        "                padding='same',\n",
        "                activation_fn=tf.nn.relu)\n",
        "\n",
        "        res = tf.contrib.layers.conv2d(\n",
        "                enc_x, channel, size,\n",
        "                padding='same',\n",
        "                activation_fn=None) + x\n",
        "    return res\n",
        "\n",
        "class ActionGenerator2(object):\n",
        "    def __init__(self):\n",
        "        self.name = 'action_generator2'\n",
        "        self.cell = None\n",
        "        \n",
        "    def create_cell(self):\n",
        "        with tf.variable_scope(self.name) as vs:\n",
        "            self.cell = tf.nn.rnn_cell.LSTMCell(256)\n",
        "            return self.cell\n",
        "\n",
        "    def __call__(self, prev_cell_state, prev_hidden_state, prev_actions, current_canvas, target_image, reuse=True):\n",
        "        if self.cell is None:\n",
        "            self.create_cell()\n",
        "            \n",
        "        with tf.variable_scope(self.name) as vs:\n",
        "            if reuse:\n",
        "                vs.reuse_variables()\n",
        "            bs = tf.shape(prev_hidden_state)[0]\n",
        "            \n",
        "            prev_actions = tf.contrib.layers.fully_connected(prev_actions, 32)\n",
        "            concat_actions_world = tf.reshape(prev_actions, [bs, 1, 1, 32])\n",
        "            \n",
        "            current_canvas = tf.reshape(current_canvas, [bs, 64, 64, 3])\n",
        "            target_image = tf.reshape(target_image, [bs, 64, 64, 3])\n",
        "            concat_canvas = tf.concat([current_canvas, target_image], axis=3)  # Does this make sense? idk\n",
        "            \n",
        "            concat_canvas = tf.contrib.layers.conv2d(\n",
        "              concat_canvas, 32, 5, scope=\"x_c_enc\"\n",
        "            )\n",
        "            \n",
        "            conditioned_canvas = concat_canvas + concat_actions_world\n",
        "            \n",
        "            for idx in range(int(3)):\n",
        "                conditioned_canvas = tf.contrib.layers.conv2d(\n",
        "                        conditioned_canvas, 32, 4, stride=(2, 2),\n",
        "                        padding='valid',\n",
        "                        activation_fn=tf.nn.relu,\n",
        "                        scope=\"add_enc_{}\".format(idx))\n",
        "            for idx in range(8):\n",
        "                conditioned_canvas = res_block(\n",
        "                        conditioned_canvas, 32, 3,\n",
        "                        name=\"encoder_res_{}\".format(idx))\n",
        "                \n",
        "            conditioned_canvas = tf.contrib.layers.flatten(conditioned_canvas)\n",
        "            \n",
        "            lstm_in = tf.contrib.layers.fully_connected(conditioned_canvas, 256)\n",
        "            \n",
        "            lstm_out, lstm_state = self.cell(lstm_in, tf.nn.rnn_cell.LSTMStateTuple(prev_cell_state, prev_hidden_state))\n",
        "            \n",
        "            fc = tf.contrib.layers.fully_connected(lstm_out, 12, activation_fn=tf.sigmoid)\n",
        "            #fc = tf.concat([tf.zeros([bs, 1]), fc[:, 1:]], axis=1)\n",
        "            return fc, lstm_state\n",
        "\n",
        "    @property\n",
        "    def vars(self):\n",
        "        return [var for var in tf.global_variables() if self.name in var.name]\n",
        "\n",
        "class DiscriminatorConditional(object):\n",
        "    def __init__(self):\n",
        "        self.x_dim = 64 * 64 * 3\n",
        "        self.name = 'lsun/dcgan/d_net'\n",
        "\n",
        "    def __call__(self, x, target, reuse=True):\n",
        "        with tf.variable_scope(self.name) as vs:\n",
        "            if reuse:\n",
        "                vs.reuse_variables()\n",
        "            bs = tf.shape(x)[0]\n",
        "            x = tf.reshape(x, [bs, 64, 64, 3])\n",
        "            target = tf.reshape(target, [bs, 64, 64, 3])\n",
        "            x = tf.concat([x, target], axis=3)\n",
        "            conv1 = tcl.conv2d(\n",
        "                x, 64, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu\n",
        "            )\n",
        "            conv2 = tcl.conv2d(\n",
        "                conv1, 128, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv3 = tcl.conv2d(\n",
        "                conv2, 256, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv4 = tcl.conv2d(\n",
        "                conv3, 512, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv4 = tcl.flatten(conv4)\n",
        "            fc = tcl.fully_connected(conv4, 1, activation_fn=tf.identity)\n",
        "            return fc\n",
        "\n",
        "    @property\n",
        "    def vars(self):\n",
        "        return [var for var in tf.global_variables() if self.name in var.name]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_1xjLRo8S5TM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class AdversarialGraph(object):\n",
        "  def __init__(self, max_seq_len=8, painter_type=\"VAE\", connected=True, gpu_mode=True, graph=None):\n",
        "    self.painter_type=painter_type\n",
        "    self.connected = connected\n",
        "    self.action_generator = ActionGenerator2()\n",
        "    self.d_net = DiscriminatorConditional()\n",
        "    self.data_dispenser = CelebADispenser()\n",
        "    self.max_seq_len = max_seq_len\n",
        "    \n",
        "    self.gpu_mode = gpu_mode\n",
        "    if not gpu_mode:\n",
        "      with tf.device('/cpu:0'):\n",
        "        tf.logging.info('Model using cpu.')\n",
        "        self._build_graph(graph)\n",
        "    else:\n",
        "      tf.logging.info('Model using gpu.')\n",
        "      self._build_graph(graph)\n",
        "    self._init_session()\n",
        "  \n",
        "  def get_random_batch(self, batch_size, train=True):\n",
        "    random_targets = self.data_dispenser.get_random_target(batch_size, train=train)\n",
        "    random_targets = random_targets.astype(np.float)/255.\n",
        "    return random_targets\n",
        "    \n",
        "    # sanity check\n",
        "    #self.target_np = self.mnist_env.real_data[1].reshape([1, 64, 64, 1])\n",
        "    #self.target_np_batch = np.tile(self.target_np, [batch_size,1,1,3]).astype(np.float)/255.0\n",
        "    #return self.target_np_batch\n",
        "  \n",
        "  def _build_graph(self, graph):\n",
        "    if graph is None:\n",
        "      self.g = tf.Graph()\n",
        "    else:\n",
        "      self.g = graph\n",
        "    \n",
        "    # Set up graphs of GAN\n",
        "    if self.painter_type == \"VAE\":\n",
        "      self.painter = ConvVAE(z_size=64, batch_size=100, \n",
        "                         learning_rate=1e-4, kl_tolerance=0.5, \n",
        "                         gpu_mode=self.gpu_mode, is_training=False, \n",
        "                         reuse=False, graph=self.g)\n",
        "    else:\n",
        "      self.painter = ConvGAN(learning_rate=1e-4,\n",
        "              is_training=False,\n",
        "              reuse=False,\n",
        "              gpu_mode=self.gpu_mode,\n",
        "              divisor=4,\n",
        "              add_noise=False,\n",
        "              graph=self.g)\n",
        "    self.painter.close_sess()\n",
        "    with self.g.as_default():\n",
        "      print(tf.global_variables())\n",
        "    \n",
        "    with self.g.as_default():\n",
        "      self.target_image = tf.placeholder(dtype=tf.float32, shape=[None, 64, 64, 3])\n",
        "      \n",
        "      batch_size = tf.shape(self.target_image)[0]\n",
        "            \n",
        "      # Prepare loop vars for rnn loop\n",
        "      canvas_state = tf.ones(shape=[batch_size, 64, 64, 3], dtype=tf.float32)\n",
        "      actions = tf.zeros(shape=[batch_size, 12], dtype=tf.float32)\n",
        "      action_cell_state, action_hidden_state = self.action_generator.create_cell().zero_state(batch_size=batch_size, dtype=tf.float32)\n",
        "      i = tf.constant(0)\n",
        "      initial_ta = tf.TensorArray(dtype=tf.float32, size=self.max_seq_len)\n",
        "      initial_canvas_ta = tf.TensorArray(dtype=tf.float32, size=self.max_seq_len)\n",
        "      loop_vars = (\n",
        "          canvas_state, actions, action_cell_state, action_hidden_state, \n",
        "          initial_ta, initial_canvas_ta, i)\n",
        "      \n",
        "      # Just make the graph variables so we can use it in the loop. TODO: Can't I create this in the loop itself?\n",
        "      self.action_generator(action_cell_state, action_hidden_state, actions, canvas_state, canvas_state, reuse=False)\n",
        "      \n",
        "      # condition for continuation\n",
        "      def cond(cs, ac, acs, ahs, i_ta, c_ta, i):\n",
        "        return tf.less(i, self.max_seq_len)\n",
        "      \n",
        "      # run one state of rnn cell\n",
        "      def body(cs, ac, acs, ahs, i_ta, c_ta, i):\n",
        "        i = tf.add(i, 1)\n",
        "        \n",
        "        prev_ac = ac\n",
        "        ac, (acs, ahs) = self.action_generator(acs, ahs, ac, cs, self.target_image)\n",
        "        i_ta = i_ta.write(i-1, ac)\n",
        "\n",
        "        def use_whole_action():\n",
        "          return ac\n",
        "        \n",
        "        def use_previous_entrypoint():\n",
        "          # start x and y are previous end x and y\n",
        "          # start pressure is previous pressure\n",
        "          return tf.concat([ac[:, :9], prev_ac[:, 4:6], prev_ac[:, 0:1]], axis=1)\n",
        "        \n",
        "        if self.connected:\n",
        "          ac = tf.cond(tf.equal(i, 1), true_fn=use_whole_action, false_fn=use_previous_entrypoint)\n",
        "        else:\n",
        "          ac = use_whole_action()\n",
        "        \n",
        "        decoded_stroke = self.painter.generate_stroke_graph(ac)\n",
        "\n",
        "        darkness_mask = tf.reduce_mean(decoded_stroke, axis=3)\n",
        "        darkness_mask = 1 - tf.reshape(darkness_mask, [batch_size, 64, 64, 1])\n",
        "        darkness_mask = darkness_mask / tf.reduce_max(darkness_mask)\n",
        "        \n",
        "        color_action = ac[:, 6:9]\n",
        "        color_action = tf.reshape(color_action, [batch_size, 1, 1, 3])\n",
        "        color_action = tf.tile(color_action, [1, 64, 64, 1])\n",
        "        stroke_whitespace = tf.equal(decoded_stroke, 1.)\n",
        "        maxed_stroke = tf.where(stroke_whitespace, decoded_stroke, color_action)\n",
        "        \n",
        "        cs = (darkness_mask)*maxed_stroke + (1-darkness_mask)*cs\n",
        "        c_ta = c_ta.write(i-1, cs)\n",
        "        \n",
        "        return (cs, ac, acs, ahs, i_ta, c_ta, i)\n",
        "      \n",
        "      final_canvas_state, ac, _, _, final_ta, final_canvas_ta, _ = tf.while_loop(cond, body, loop_vars, swap_memory=True)\n",
        "      self.final_canvas_state = final_canvas_state\n",
        "      self.last_actions = final_ta.stack()\n",
        "      self.intermediate_canvases = final_canvas_ta.stack()\n",
        "      \n",
        "      real_score = tf.reduce_mean(self.d_net(self.target_image, self.target_image, reuse=False))\n",
        "      generated_score = tf.reduce_mean(self.d_net(final_canvas_state, self.target_image))\n",
        "      \n",
        "      real_score_summ = tf.summary.scalar('real score', real_score)\n",
        "      g_loss_summ = tf.summary.scalar('generated score/g_loss', generated_score)\n",
        "      \n",
        "      self.g_loss = generated_score\n",
        "      reconstruction_loss = 10*tf.reduce_mean(tf.square(final_canvas_state-self.target_image))\n",
        "      recon_loss_summ = tf.summary.scalar('recon_loss', reconstruction_loss)\n",
        "      self.g_loss = self.g_loss + reconstruction_loss\n",
        "     \n",
        "      self.d_loss = real_score - generated_score\n",
        "      \n",
        "      real_min_gen_summ = tf.summary.scalar('real - generated', self.d_loss)\n",
        "      \n",
        "      epsilon = tf.random_uniform([], 0.0, 1.0)\n",
        "      x_hat = epsilon * self.target_image + (1 - epsilon) * final_canvas_state\n",
        "      d_hat = self.d_net(x_hat, self.target_image)\n",
        "      \n",
        "      # TODO: This might be wrong. might need to calc gradients wrt xhat concated with target\n",
        "      ddx = tf.gradients(d_hat, x_hat)[0]\n",
        "      print(ddx.get_shape().as_list())\n",
        "      #ddx = tf.sqrt(tf.reduce_sum(tf.square(ddx), axis=1))\n",
        "      ddx = tf.sqrt(tf.reduce_sum(tf.square(ddx), axis=(1, 2, 3)))\n",
        "      ddx = tf.reduce_mean(tf.square(ddx - 1.0) * 10.0)\n",
        "      \n",
        "      self.d_loss = self.d_loss + ddx\n",
        "      \n",
        "      gradient_penalty_summ = tf.summary.scalar('gradient penalty', ddx)\n",
        "      d_loss_summ = tf.summary.scalar('actual loss', self.d_loss)\n",
        "        \n",
        "      self.d_adam, self.g_adam = None, None\n",
        "      with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
        "          self.d_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9)\\\n",
        "              .minimize(self.d_loss, var_list=self.d_net.vars)\n",
        "          self.g_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9)\\\n",
        "              .minimize(self.g_loss, var_list=self.action_generator.vars)\n",
        "        \n",
        "\n",
        "      # initialize vars\n",
        "      self.init = tf.global_variables_initializer()\n",
        "      \n",
        "      #summary ops\n",
        "      real_im_summ = tf.summary.image('real_images', self.target_image, max_outputs=3)\n",
        "      gen_im_summ = tf.summary.image('generated_images', final_canvas_state, max_outputs=3)\n",
        "      self.summary_op = tf.summary.merge([real_im_summ, gen_im_summ, d_loss_summ, gradient_penalty_summ, \n",
        "                                          real_min_gen_summ, g_loss_summ, real_score_summ,\n",
        "                                          recon_loss_summ,\n",
        "                                         ])\n",
        "      \n",
        "  def train(self, batch_size=64, num_batches=1000000, last_actions_arr=[0, 1, 2], start_batch=0):\n",
        "    train_writer = tf.summary.FileWriter('logdir', self.g)\n",
        "    start_time = time.time()\n",
        "    for t in range(start_batch, num_batches):\n",
        "        d_iters = 5\n",
        "\n",
        "        for _ in range(0, d_iters):\n",
        "            _, summ = self.sess.run((self.d_adam, self.summary_op), feed_dict={self.target_image: self.get_random_batch(batch_size)})\n",
        "\n",
        "        rand_batch = self.get_random_batch(batch_size)\n",
        "        _, last_actions, fcstate, summ = self.sess.run((self.g_adam, self.last_actions, self.final_canvas_state, self.summary_op), feed_dict={self.target_image: rand_batch})\n",
        "        last_actions_arr[0] = last_actions\n",
        "        last_actions_arr[1] = fcstate\n",
        "        last_actions_arr[2] = rand_batch\n",
        "        train_writer.add_summary(summ, t)\n",
        "\n",
        "        if t % 100 == 0:\n",
        "\n",
        "            d_loss = self.sess.run(\n",
        "                self.d_loss, feed_dict={self.target_image: self.get_random_batch(batch_size)}\n",
        "            )\n",
        "            g_loss = self.sess.run(\n",
        "                self.g_loss, feed_dict={self.target_image: self.get_random_batch(batch_size)}\n",
        "            )\n",
        "            print('Iter [%8d] Time [%5.4f] d_loss [%.4f] g_loss [%.4f]' %\n",
        "                    (t, time.time() - start_time, d_loss, g_loss))\n",
        "            \n",
        "        if t % 100 == 0:\n",
        "            self.save_model('adversarial_global_vars_celeba')\n",
        "\n",
        "  def _init_session(self):\n",
        "    self.sess = tf.Session(graph=self.g)\n",
        "    self.sess.run(self.init)\n",
        "  def close_sess(self):\n",
        "    self.sess.close()\n",
        "    \n",
        "  def load_painter_checkpoint(self, checkpoint_path='tf_conv_vae', actual_path=None):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      if self.painter_type == \"VAE\":\n",
        "        pth = 'conv_vae'\n",
        "      elif self.painter_type == \"GAN\":\n",
        "        pth = 'conv_gan'\n",
        "      saver = tf.train.Saver(tf.global_variables(pth))\n",
        "    ckpt = tf.train.get_checkpoint_state(checkpoint_path)\n",
        "    if actual_path is None:\n",
        "      actual_path = ckpt.model_checkpoint_path\n",
        "    print('loading model', actual_path)\n",
        "    tf.logging.info('Loading model %s.', actual_path)\n",
        "    saver.restore(sess, actual_path)\n",
        "\n",
        "  def save_model(self, model_save_path='adversarial_global_vars_celeba'):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    checkpoint_path = os.path.join(model_save_path, 'graph')\n",
        "    tf.logging.info('saving model %s.', checkpoint_path)\n",
        "    saver.save(sess, checkpoint_path, 0) # just keep one\n",
        "  def load_checkpoint(self, checkpoint_path='adversarial_global_vars_celeba'):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    ckpt = tf.train.get_checkpoint_state(checkpoint_path)\n",
        "    print('loading model', ckpt.model_checkpoint_path)\n",
        "    tf.logging.info('Loading model %s.', ckpt.model_checkpoint_path)\n",
        "    saver.restore(sess, ckpt.model_checkpoint_path)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "inDRcRgQYF_e",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Set up Tensorboard"
      ]
    },
    {
      "metadata": {
        "id": "49mnMb4bWF2N",
        "colab_type": "code",
        "outputId": "71f967f0-109f-4bb9-90d0-4aeb7be71a41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "#!rm -r logdir\n",
        "\n",
        "\n",
        "get_ipython().system_raw(\n",
        "    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "    .format('logdir')\n",
        ")\n",
        "\n",
        "get_ipython().system_raw('./ngrok http 6006 &')\n",
        "\n",
        "!curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "    \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "https://ff622d71.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FYqPzjOA029A",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Iv8bNS4BYInS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Actual training"
      ]
    },
    {
      "metadata": {
        "id": "G518ssqYLh8D",
        "colab_type": "code",
        "outputId": "6b2ccaeb-b801-41f4-ebe6-72d9ef7a50b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialize the graph. This step will also preprocess all CelebA images to 64x64 the first time you run it.\n",
        "lol = AdversarialGraph(max_seq_len=15, connected=False, painter_type='GAN')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m04-13 11:37:31 \u001b[0m\u001b[1;31mSkip making directories: data\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Model using gpu.\n",
            "INFO:tensorflow:conv_gan using gpu.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "[<tf.Variable 'conv_gan/lsun/dcgan/g_net/fully_connected/weights:0' shape=(12, 4096) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/fully_connected/biases:0' shape=(4096,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/BatchNorm/beta:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/BatchNorm/moving_mean:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/BatchNorm/moving_variance:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose/weights:0' shape=(4, 4, 128, 256) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose/BatchNorm/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose/BatchNorm/moving_mean:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose/BatchNorm/moving_variance:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_1/weights:0' shape=(4, 4, 64, 128) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_1/biases:0' shape=(64,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_1/BatchNorm/beta:0' shape=(64,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_1/BatchNorm/moving_mean:0' shape=(64,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_1/BatchNorm/moving_variance:0' shape=(64,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_2/weights:0' shape=(4, 4, 32, 64) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_2/biases:0' shape=(32,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_2/BatchNorm/beta:0' shape=(32,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_2/BatchNorm/moving_mean:0' shape=(32,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_2/BatchNorm/moving_variance:0' shape=(32,) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_3/weights:0' shape=(4, 4, 3, 32) dtype=float32_ref>, <tf.Variable 'conv_gan/lsun/dcgan/g_net/Conv2d_transpose_3/biases:0' shape=(3,) dtype=float32_ref>]\n",
            "WARNING:tensorflow:From <ipython-input-9-9225df588c5e>:30: __init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.py:1624: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.flatten instead.\n",
            "INFO:tensorflow:Summary name real score is illegal; using real_score instead.\n",
            "INFO:tensorflow:Summary name generated score/g_loss is illegal; using generated_score/g_loss instead.\n",
            "INFO:tensorflow:Summary name real - generated is illegal; using real_-_generated instead.\n",
            "[None, 64, 64, 3]\n",
            "INFO:tensorflow:Summary name gradient penalty is illegal; using gradient_penalty instead.\n",
            "INFO:tensorflow:Summary name actual loss is illegal; using actual_loss instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Deprecated in favor of operator or tf.math.divide.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SWEM3sAHYTaq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "65ee0aa5-4834-4224-e225-041418f7d7c9"
      },
      "cell_type": "code",
      "source": [
        "# Load GAN painter checkpoint\n",
        "lol.load_painter_checkpoint('tf_gan3')\n",
        "\n",
        "# Load VAE painter checkpoint\n",
        "# lol.load_painter_checkpoint('tf_vae')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('loading model', u'tf_gan3/gan-571445')\n",
            "INFO:tensorflow:Loading model tf_gan3/gan-571445.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to check for files with this prefix.\n",
            "INFO:tensorflow:Restoring parameters from tf_gan3/gan-571445\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Zi5PVNkjT4Ko",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Resume training from checkpoint\n",
        "#lol.load_checkpoint('adversarial_global_vars_celeba')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vvR_By58YTWb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "last_arr = [None, None, None]\n",
        "lol.train(last_actions_arr = last_arr, start_batch=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JuDRb9-4YTTR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "821af323-ebe6-41f1-b6d6-846744c7a19f"
      },
      "cell_type": "code",
      "source": [
        "lol.save_model()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:saving model adversarial_global_vars_celeba/graph.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "il9R-sg9e_ui",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# evaluate model"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "4cYl4noTGgv-",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_images(images):\n",
        "  h=args.screen_size\n",
        "  fig=plt.figure(figsize=(16, 16))\n",
        "  columns = len(images)\n",
        "  rows = 1\n",
        "\n",
        "  for i, img in enumerate(images):\n",
        "    img = img[:, :, :3]\n",
        "    #print(img.shape)\n",
        "    fig.add_subplot(rows, columns, i+1)\n",
        "    plt.grid(False)\n",
        "    plt.imshow(img)\n",
        "  plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "KPsvuD2fGgeK",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2FhXOE7beodx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_realEnv = ColorEnv(args, paint_mode=PaintMode.JUMP_STROKES)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NGE8ysZvhIoP",
        "colab_type": "code",
        "outputId": "cc0e4049-98b4-46c7-cd12-a76392baa86c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 900
        }
      },
      "cell_type": "code",
      "source": [
        "def run_actions_on_real_env(realEnv, actions_array):\n",
        "  images = []\n",
        "  realEnv.reset()\n",
        "  for ac in actions_array:\n",
        "    print(ac)\n",
        "    realEnv.draw(ac.astype(np.float64))\n",
        "    images.append(realEnv.image)\n",
        "  plot_images(images)\n",
        "run_actions_on_real_env(_realEnv, last_arr[0][:, 41, :])\n",
        "plot_images(last_arr[1][40:48])\n",
        "plot_images(last_arr[2][40:48])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.38454667 0.96535766 0.09308344 0.9505116  0.17724606 0.30450547\n",
            " 0.6502888  0.4402511  0.2604816  0.96645355 0.26694798 0.9286592 ]\n",
            "[0.68373114 0.97992605 0.03862584 0.98738176 0.19753921 0.06570596\n",
            " 0.36650392 0.34395814 0.24923494 0.9581851  0.7779903  0.9496584 ]\n",
            "[0.623242   0.96637833 0.07596883 0.99238086 0.23069051 0.06583923\n",
            " 0.11597264 0.14208579 0.10533959 0.9112005  0.95347106 0.9155514 ]\n",
            "[0.4510596  0.89982545 0.18692341 0.98886955 0.26760012 0.10582417\n",
            " 0.09530315 0.08757928 0.05431858 0.80737627 0.9726361  0.86161155]\n",
            "[0.4520964  0.80713403 0.29943508 0.981478   0.3502741  0.10938773\n",
            " 0.3089844  0.20305628 0.13653481 0.68460107 0.94438267 0.8807312 ]\n",
            "[0.54394615 0.89195734 0.4625056  0.9515368  0.4420282  0.0997746\n",
            " 0.8228282  0.5277697  0.29406476 0.5657304  0.894259   0.93375814]\n",
            "[0.5510128  0.93559325 0.704708   0.8514097  0.44867465 0.10849419\n",
            " 0.7613071  0.4423803  0.26289487 0.5570793  0.8630388  0.9332855 ]\n",
            "[0.5001207  0.96276057 0.8696755  0.55073684 0.5430363  0.10913771\n",
            " 0.50844604 0.2374163  0.16950995 0.6889672  0.8287027  0.93001944]\n",
            "[0.43886706 0.97569484 0.90904975 0.22378278 0.55672324 0.11424014\n",
            " 0.20243177 0.11466083 0.09536567 0.7987414  0.81676304 0.91858983]\n",
            "[0.44798967 0.97747445 0.84011656 0.11391383 0.4450995  0.16335225\n",
            " 0.0815798  0.05663642 0.04440993 0.8286923  0.8071805  0.8544279 ]\n",
            "[0.4334524  0.9769021  0.62628657 0.08564508 0.3140415  0.24053746\n",
            " 0.06158346 0.04545575 0.03329057 0.8769171  0.7747709  0.6307055 ]\n",
            "[0.5123017  0.96733105 0.43891218 0.06696901 0.19300371 0.32167405\n",
            " 0.0588938  0.03818947 0.02906093 0.8975873  0.6426226  0.4665448 ]\n",
            "[0.8182941  0.945211   0.24346787 0.04477969 0.48456326 0.10154447\n",
            " 0.07519335 0.04656112 0.03729564 0.7846495  0.2544039  0.43732435]\n",
            "[0.9370908  0.93127286 0.03802586 0.03632492 0.93776095 0.01191521\n",
            " 0.14879292 0.10743865 0.09433985 0.13804826 0.13220546 0.45714998]\n",
            "[0.92069864 0.9241041  0.02209759 0.03923169 0.9333738  0.00946745\n",
            " 0.06552759 0.04993448 0.04736316 0.12641934 0.20578894 0.5003675 ]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cYiD2HX51Tuhb/HqBWZ6lI15cSdQmHW+iLbas30i8EnKE2Gp88AQfMxldY2yB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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 15 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yrUJZx60xsGnvN20jkFMoEsbt6ATMBpOH48R8y0Gg/4EZHMrxDOWt1whQZP8F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\neETZT7/+yy/xhcBxd1cxS9yStw059D5qVeL2i4hJ3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F0YeKM2LMwafDGmU3NzjBBqWh39\noOwIuvebzbFDuLfnEu4121uV4y3fWlgQNRjWA1ohJH2PNX1e+wuXkuWU8ap2dohLUKq8MKueiJ2c\nljMsOeHQ1gSCBtbXZbmnCL7W4G9YnLac4ZwQWm11iq27GRIDoQaMXAjYV6qwsUbLRzhzn3+ZwSlt\n1I+7Mi1tqOnWOZDe/6x+0ex72VFMSWm6hkh1EIhI9S/uZUU22KpYbxWPDZFO2033TX0f0Mu73gSD\nmjswMDAwMDAwMDAwMDDwoHhY1VwAd3eFqndezgg1+nleekNpgmgWNBpo9I45Ke3TkYdp0XdjcFWz\nEYd5wk2lcb13dYUn1yV69ORwo7QOFu7CQNbATlWAYX+lPYpzitrcPsYAEqiIijOC1k2wZK5qNoc6\nBVZ7oaFSZOs1WGO0ENs7i1gjpcQ9/Z1jUmoTkWxS8Ba+Ru4PT97Dk6elgb3fXeGq9RSjnq1Yzme8\nfPGiXs9li/iLgnGnCbS0C+fcIyMbBdPJdXEab43SuPauNy2/159wo4JrCZpNdd61nuoIibWH2eQ9\nQn2U5+mgn89h6SJGOQHUI6zz7EE185lz1pCMzb2vk7cGc7XXdNjhZW0unNLUC8Fz1gwqoRe8l8Bu\njxRuI8FbMYYtWpTVkNFrttZqRDP+LtgxbSJu+tRy1mzPbJ02Ur+eZqWKG2tVLGaJAbc1sh6EVYFv\nCStcm0c5d+EtMSoI5Yi7oAxBsyDGWriaoVxPdxrdZ87gENBibc5apQmRMT2TvKF9WzfrPSTnu1o1\noPN8q4xqrFVxo7iuPWKJTZQ7RVzdNCq5KP3PWIu5qeO5SdeCIm5USwta/7cLoagCVhXrlFRsxuYM\n34SmhDXzZ6YZ1NYk12ldYNaoMOekWS7nfb8+iNJpt6rDYTkrzckZ2/slkwF0bTur+qmwQFJUeoBB\nF4si9Mwdcb/n1jpdb4mg9oVIF0eC18/Mwpr5jDFibfQp51SMyh0OSE0kxFpwzfAkFzQCvdzdaX9R\nyhmhRsKpRrUvhTIf2zMS4GsfNmNsp5kTYfY1k+dnpdOvyxGxPgOnVy8085ziqqtMDKtmR9flvBED\n85jeL32AhYB8rivBetY90cQAW2ed5QyukyLHAHivVMWiBttp8Lo3bNRrzSb4btBFS5yxPVvHolRe\n46d74ivtZ04ZoaqD2sO1iqywXXD78kX9LgP59FMAwGeffKLMl1cvXigT4MmzZz/cIF8RZsOMoY1Q\nHyCaJfDW4tnTsm9/+70bPLspz9J+I5QyOYO4VhXNZcU5tGw96RxnhlI3QUCmSrtbAybXegJP8FXo\nkXZ7hCr0GM/Hrj4eAuxGZdtaiyX2vbllww2AY+2TKGw0W7RzVoUEZ2c7w4ugYixFs4/0vuhe+flM\nTEvSGHTBNHRVVUk9tUwZKsrYsrOXAlEXmzMbf8ZC1F9zRJhrZn1yFr7SIa23SnG1ZCCNaRKjXp+Z\nOt0Ykou6MQCY3pscMUKVjR3BVsYEuakLBMagtHRividKBED3sm3imYCy/gIgssoeLIJL1a+yhMPU\nKPGFKlqG10uNZmf6c4Juy8SiIp1MgvZk5Q2ttBe33BcuCvHy1Nzm21gqmV4AmAnYVz/zyjocdrVU\njjrzpOjzVP8C0kseEqPtOM4aCPfr49aUGUZ7r4Z4hpnKecSZCbvKGIpigLpupXWB8U31OsN5CxfK\nd99c73F7bMyintl23iLUfqGHq50+NzcHo+83EJ0jhgiLimV1P8cY07Pcm/m47R2aNiUMgu7r3hMl\n+jyz7w3xwNRcdMclRa1HYu4HGIEoXXM37xBau4glYqoO0ASHVCfhdsJlZqUCZuFO/d00jcXklIJH\nsEiV/rS+fqVUSs4BUpsII0ZwWmGkOT29BmnaH5QOJTnCSJU4z10RL4akB1RrCNO0UWusE9sLen2q\nY6yt0XAMWhMZY1CaWU5B6Z1XhyvM9fDtJg/TnAgCPqwL2+Vrmehe2p83NlAV4Q012BqjK84aIqbm\n4Fqjv2uoL75ZRB0bon5AY+5KvMkIQp385yTayDc9/ww3leYkMYJqg+94XmCJQSprSzrpr57sK00E\nIJfwlFtdCLS1xXFNmOpArmeHcyzXMLEFodOtVWmTe5BiW4F0j4a/obIQ0Yam3PcMSwRXN7frq8tS\nyLaOUURfOCxoM3cyAjePsNfmpJwhfOpjr6+vy6oHzrBRccsiergW9HqnxBmhOScgpcAfX79Q1cac\nIhq5nHMCpN9Tdk4PxMbYflCR7vjmFLpiZwaabp8xtrf0MKbI26P4OK0+T0SwtgbzOenny6aJtXVO\nn/VGHwOAq/1hs66VdhsAlHJ8KYj0EgFOEVLXGDfNqF08kM5HdY73ux1MdVhjWOHqAdVdHVSd+Hw+\nqap3iBE1XockgqnVcjuntErOCbGpdOYEtLZA56NS64ticR1zSqX2r/69BBH64VjpZOjBgpSz7pKS\nkjo3GzYfyHYVUU5ZHRDJGUulNsJPqoB5Pc269qZlQWgHehENFJ7PZ4S6hobjSWum9xcueWBhfTZK\njWjdZ/xOHSPmjMyt/ML2VhAQUKVlToeDBpjCKagzz+hN7I21mOp6cm0+0P1xPS+w9dCSXn4GqkFi\nCQFCrZ63B1dzTrCGwFUhFDZr7ZnZXWngD2HVoKNh3ijDd+V5Qp93vCnJEGSYRkE2RlsrpJy0FYnz\nM6Q6sEsIWFqriWVFqOr5ObPWLb347FM8re3dWtDkUhBsynxENqqqnfa4Vd0ma5Dr+vT6eAJirXVF\nRqzXsRxPyG0vOp0w1WUmcYat9aWwFu1KOGWsdU+bXQY1+97dKk0yxQDXAkNhQUqpn1WM02DrfHWA\n2EoTzxlPWr0oR2Rb5sirmGDrujpZg9BqeiEIzYfZtHwwRBpMLGUs1W/ojE50NQUADKWicmYtaZbE\nGlBxdGk18n4Al8zI1Xbe0MYnYaWEs+2tvmJIALUAVy87ypxhbDu0JHCNtuSce2Da9HaGnDMSKm0W\nBPYlmHZeV+TWpiqnXmsfE5CTHjxKiUpNlkyb9cp0hV/JAVB/NUPq9ew94bArn+uc0wOKI+DcSiTQ\nS4doQ7Z2ZuvLbFouofsZ5ef6HqJS4wxoDeelwCLqUwt3O8IaPS+knJFa+djkVbU4Jgaq6jab5ukB\npzUofTpDkOtRKoQAg42qeX3PmhJeHmtrFSK4XfFtbk9HTR7FNarPwjHAWYKvp+bMJTAFALv9vvtq\nzqlPWXzuql+wRvj60D273un9jznh7lTrt3M/azCzUoqpr9pwxnQfFXSvfKvBmPt+vK3jPNQyyTfB\noOYODAwMDAwMDAwMDAwMPCgeNiMKwV0VwDie7nCsGY8QgwrMGCJY2wQpLMK5RIAsGaWTee8wNdWu\nHFW9cz/vsKvKVzdX16peCUNIraGP6zSfkimsdIup9xo0BFWGRIqgHEGo0SNh+Kl8XwirUu9Kj8fG\nuTCANtTuKlUxRVV9lBAxTa2HntHv5hi176e1TpVkgc5nksxYKs1svr4pKlwAXt++0ozrq9fPlcLV\n6DOXgkCU3pgz95Q9b2i6G0qSNWYj6tT7aJIxem8SEnYtoj95zK0YG6INnXezUyEY66AF4olZ6QZ+\nctrnjzIj1mg4geFni2sq9yqEjLn1YmN0gaOYMDd6BAhoDcKxamby+XFFaBGmlLGrvxtSxjk15cCk\nUaPwBSpwgp7Zo238lzolyVmrioqTs7/zQ74mmriVSI/aWmxoF8YqNSgSVBjDEmGpWaI1Jyw1sp6Y\nMVGfvy2i7JzDrk2PlOBVBXQjciOifSlZuviKAbAon1nuUd+YGaY+E2RtTzkboyqVxntwU+M0ps8p\nY7qQl3RaMFurEWayFr4uBTBtFShZ1qWuTTCEF59+DwDgd3v4plbNjKk+Y271em2X7iMKoh6hNAa+\nRXatKX3lUOkyLZtrndISrXNKNyLvcW7RWelqtTLvVLDJzzPc3AThpCviTpPO/XA+qsK0+Emz0cLS\n11WRQtFsUfnIul7LRqkwp6SN5EHU+wIzKyVR6L4KbrNvzkGpnimsm8h9F12I66pU/tP5jDV+v3ym\nsfrM3L5+hSfvlVKI2U8q0MQXViMHoOrQ3k6aQTebfrSWrGYQQ1w1kyGZsbYsdAwIuVHIwoYezpop\ndZPTVrKggKmKc/j9HktVtk8xYWqUPclAqPfeW+SW+ScCUxeOQlhhtPSg31uOnU1QMlgtpSRqX7IW\ntl5zZFaGiGxEPJbTCWndKJDWVWt58VyZERkW5IqfcfvqVkXFRIDDTRFoijFohvZ1pfFeCgTSuWDb\nF6OJfdT32K7QLDAquuQmj3NtSr9CuhhTTrA1U5QNKTPIz100jY1BbFkr51VQL774DGstnbCT1z6K\nYFYBJCNcSp+0D+aCfaVBMDPmuvcsYcWu9qyMxJhtp6s21dGQGHPb1zOrYiltGEAi6PRiAXxT2dWO\nlihrsq7zWWnoyAJT/QOyBLK/W/Oxk0ctQZ9Ht6Veg3qfWGvVLnZbdkSktGFLKIpAKPZql+Qmp9Rc\noo0vag2al8DrCa1rpBDdyzI2eqZFYQXqHBTWDBU4K800hQhsaPA61pQ0i7cEUV+UOeHZVfVDDNTu\nmVmF1O4aKwLlXrW/ZWb1XYuv2NlQzefZqrf337wcmi86WaNj39su2ARrNZstziE0UUxDWNdGMQZe\nL00kK6Pl8YgZiJ1pYqufaJzVMiVyvezg9tUrpNxE+7oaLjK0dMUYwHur+3pKjN1c5xRzl/YUUaqx\nAOo3cyQcqt8YU8aTQxXByxm7puqfkvamf3VatfQx5r7/emN1zjJ3On1j/pXv3ZQQklHWR6MPvwke\n9CBKIDyrGwGEVRUwpC73LRAsddI6Q3hyaBvHirDUCcmC6+oMiUxaL5pS0KbbxjmligizUhDFOFWr\nK415G/2HtD0MSQJyU1dllBWxLd69Nom4tyixBN0Iw3nR7yDZcqi7c20NQdCa1QZt91KcsNbGgCGt\nzmDqDdnvjncwr4sqXzJWa0qXdYGrTtLpdIfVrvU+XtbMBMKz63IYfwUoLXZTagmJUKdHoijdwts+\n3pxzP3CS0wWIc8a8rzVRKA5v+QfGob6+c6TOmWSB5OLA5Oggte7F5LjZ7C0iA1w3UTOT0rqIU6nF\nADBZILcD39obge8cNeYgdgb6zO19p19YErTyBme6/D1Lp31uBXQN4R5vlzeUFXW4hZVm8+TZk99p\njK8BIsJ7dT6+un2tB6Wcu4Ipg3Cs8zSdGDetTnneb5wKVtrtYZ71gJoAdUgoE6h+DhFpy6VMAKq9\niLvDCSJVFyRIb+tAdC/IYdDp/rIJhBAZVYeLMfY2TRsqL+e0qfOwWtu2hlXpdNZaTLUu1lqL2BT3\nUkTbXZcUQKFcw6cvvo8PW32zdZtaqa4WaC48HwHg+mmpc7t79RJhQx8+tGCLsDq1WV7j+kl5v59n\nrS/NKekhk4iQa0AnQmAO3y6vW9uVTZ3bqO8Cqans5gSY/npzziTF3lqFCDnlfggx3ekTiLZNKOrW\n9WM3h0njvDZ3jyzILeDkrNZvLXe3qnKcQ+gUyc+1jWoUyeXVS3XUMkotLQAcb2/7YVgYse5D/sJq\nqwBwffVUr7u1+SGQHkoZ3Fu8gHRtN8YA2kLFwVc74gZaApNiUDouAwg1mCnU1x7nJ20Dk08nVfvk\njdqiAfp+bQyWBF0/rbOqiDsZ1to4Yy1gW+1oBnO//91LTYioz5Z1SsflcEZKp3oNvV0EE5DroZSm\nCW33WZcjQirP4rIGmPq9MUa8flVUUFNOeP26/Hy4uflCe3wlEDBXx++0oZMziwYYc8745LNyAD6f\nz/gXf/93AAB7a7BUx9eB1aYwhLXSdL0AUtu0MBFCrYPOgAalYS343OdjrnYUEg0+0raVg7EILBq8\nc2R6EJETqLVfIyC2Nk2yaiBpsgZr21A53w9wai2ZxiIqLbPei43yPqTTXkUEsqkv1XIMZpj6+c5Y\noJU+vQUV8I1AwFTbZ6TjgkXXgHJYAYrSe67+T8wZN3WPts5pG8Is0qmqZHWPk5zBzQUXgJp2gDFA\na78CC8OtxY4AbQ81DmiaEcywU6/Jv9c2g/phy4hoaRKhH5JyCP0QL6KBLgtRbYbZAurd+X7I3rue\nZEhs9QDHIn2eiug+yCLdL+LOw2buB+5WG39JuBokWc+rqlizCN6vdaFZRCn855TwrKof26mrhaec\ndT7SVOnXKOcFf92fvdT0BDhjuq6BaOMLBRrFT+HQFdxz7CULrfSpUZ61y4i3WFMr++o0dWMdpPr8\nKS6bOnxSv9lSD+h4a4B6L7Lp5Q+zcxu6be7PEEGDBbO3St8FQX/XoNN3nTV6VjrsBzV3YGBgYGBg\nYGBgYGBg4JHiQTOi3jl8+4MPARSRm9eVLjJPCbFlt6SramGjSptS7AIJRHqCnqYJUiPHE1gj3TlF\niC2qdM5PGo3ICUr+MNRpHeCwaXAtnVpjKs2mRfKZgSqAIykqlUVy3qhKbVPVPZm6380qjhISo4q+\nIm6ad8OwNi0WYzDvSmTm6ukH2FUV4N3+oM3cMzNe1ijv8fga1y3jTKQZrhZBvxQm7/CTP1ZUFv/J\n90TT+9uiZWzU0QpTqdEVsaGA0r1Ijb5uNzQNEaXgzpNVVc8MC7GdpnJu1BcAc40QOWP1c5IQOAfN\neuWYEJroRcga2c0iWNbWt1CQUqONkvbS2k8OT3a9oL71NkXOmFqj7cRojE7Z0pS3dAWCRii3CmPG\nGO1vdbjaK4XpXBsfXwreebz/pGRgzsupZ2BMpxNba3Vsa4qQU1WBS1EjaGtKnd4JYGcbxTL1XoDS\nKbsQQWhZ0A39SqRnpyYipTDRRo2G0VWU9QsbxcgYcKOhEym1EhvVzcxdkXXLMoBzoBpVnrzXqK3z\nvmcJvdcMAsv957tRqcg6LegHiuAaUGx6V9e783JZsSLvJ3zwYx8DKFmfU2VLwBicWuaAO81KAKXm\ngnq/W+Hek9A5B3BlMRD1+wqBbawTAtJd+a4iYFbfQgZS6QMUo1KR2fRudEWNEL3/JKSr9XFWm3LO\nm167XUZBpCsBSpHDqL8rndLtPahejxijNGl4D4llsDEl2JpFmp8+69nhGLX/6el4p8/cbvI4VdXQ\n9OrVD7HGV4c1Fta0Mo6gQnrt2oEisKX8Gma97phXZTGQMSrg0ijmAOp9aRm53mtwXVbEmuVeXr/S\n+2o5KY3TWtJ9yUmn8jFMicTXzNWUE6xtUXNWlXhsaKnMG4Eigo7JUC8/qRdS/i+k76HNHhNShq2Z\nPgeDUEV0YoYKTS3LAqliL+uyKG17f3XQzzxXFeRLYZo8fvKnfhwA8Ju/8c9wfFEyk5Z6D2PZSIyS\niPa7TssCp8yAToEjYzDtmgiZU/uKQLM01hC4MjMyu84aoa2ifyj0bjT1U61fgOGkgjYm9x6GOQb9\n/Zgybu9aSQbjFJsquqhq7ob0A0ukfUQJG2KZQPtHJxYVYjKG0EaeAc3QJpbeTWBD8S39MOv+dL7s\n/ui8xwffKoJWz/MniKeqwG1M78cL0qwVUFgoAMDJdsoucs96uc3cNJ1eC0PqYxoiiGY+75fjtJ6R\nk4V2fECOm3KnItinasOc2lIKznmz1yZoRZUAa2z+mShTzBIprdpQZ6YRWyyhC3C2z4lZVJDKEPXy\nDGyndWe+bOe+tT2zel4u769+/O1ix08+ea40ZrYWq+5Zm/0LpOtbSAlx8zy27GhRB9/Mo7a4GQPr\nm4o/qfid9773YmVGWFopGcE3pogkZe1QFfg0diNmpcKlCbn5nIgqeppyVyrOLMqybGOsw9aftyrH\n2Py8FT+kkhLV62l+qWFz77zThPXef3qNdW1nrTenyr/RQZSIngH47wD8EZSn6N8H8OsA/gcAPwng\nNwD8KRH50mILs6EJhBh1c9/Pe7hKY1jDunngM871okIIalR43x1cQJsnG4LWB+6chdQDWE4G69ro\nAAm+0ZwsKR1CJEFdMk6QujHAWJDthw7JSdXGLG9qLzlpY2PeHGBSTqpsmIXAlU8vOXVpfOqOBufY\nqWyVeAgUulHjsDMIpjpYd8tJV/f94Qq3t6U9zt3drR5Em5T/pewIiB78Us5au+hMT8vfsnQKCotK\nchNl+Pqe2XhdxIjkntJae5hnS9i1tj3otCKmjGO9/SxQJ8nkBMtNzdQqxSCCYOY9TKsFTVGdGJak\n9zaLqPKY5Kw/h8hKO8ksuoFbIrj6vB68VUXCzFbpuMydXmgNbYIUhNTo4xtH3Jiu5jZ7p4f4dk8u\nZUeRvrmXg2X5zis/4ao65+d13TQ07u2UjstJ61As+sIVNjUmkhOkNr53cVUabHFKO41LfSERbV2A\nnFUp1xrT64OIAOs6LTgzpNVvp6gbe3+1zrVGK7RO1yDiDKn1VZazLtAsrK1jcliRN7UrvU2L15qe\nlKPqBa5xxd2pOJ7XhxvYep2ZWemMVmmrl7Pj8a585+nuttMs51nroNPtK6WXMmdVSeW7W92B/P6q\nP7PLGUapaFYPro5EaUV5swGVQyLp57d2V4YiqKpHCjZlCsbCTJPOC1kDNieV3qqB+0FURHTNZGZ1\nmFJmtTWs03KGFEKnGIl0Kn+K/RkAgFhraY7HfuBm1v3GOY9cr/l0vNO2B1q9f0E7trmectT7MU/7\nTs0V1vssYFXQDXHRuWm91/rezBmmqomaPGm5yvn2jKW28SBD6tgsa0Cqz3gir3ZMWbDrzDylQ65R\nkIzX+iKTA1D3Red7fTmT1TZinJMeMMrKtyHo+rZ2ZHDqFPMWMJYYN+UwmzrL7eE4pa5Q7RzOjWq8\naS+2nE89ULtqHfXF7Pjp8xIcvjuddW+ZvFOfZ02rBl7XmPDdT8tH7q3B+/vaOstbxLrmGwKupn6t\nuC3BkMkQpqpsyhAtzTGbmjtwBpt6EFqCrrGWeklFJAu7v9K1mGIANYVl4d4uL+dCGUaxUa9XTrrH\nh9jpyI6gc9PUsgpgexyp90wpmkDaLPWbx6Q/MwLl6XIW/Uyjh+1L2ZFxqs/OGmJfq6zR2vgYkqr/\nJmY9RNG6wldpY+MMrCqKE6QlPKRTtY01qrbKWZSiCdNrKiML2qZrUoY1PTjYHqYEA/KzHnpMzjAb\nDQ+9/yKqPEzc7Rtz38utoUrJrWVEbb+fDGKqAffsEHMLhvUA7JYaSgRNuuTtGr4JKBRdg3bI1j3l\nInZkFnz6qtaMn/q8y5YQqz8Y14ibVtKRBTjXffvu1H1zYzTpYjc0+whAWsknAfsWeI0Ck+teFHNX\n1BdWHRzeJMAgDKmviwgiHFzbR3PW+2m4P/M5sx5QOfW2PeuadC8v9c31ojc/86ZNYFu/633XIKV3\nPfDpnWmlsJjcpt0L+gH1W8+e4O5YyyjydoZ/Od6UmvvfAPjfRORfBvBHAfwagJ8H8Msi8ocA/HL9\n+8DjxrDju4Fhx3cDw47vBoYd3w0MO74bGHZ8NzDs+HsEPzIjSkRPAfwJAH8GAEQkAAhE9LMA/s36\ntl8E8H8D+M++7LMEPauTcoazrUeRh+OeXUgbZcWlUvhmY3BVhVI+ePY+nj0pqfZwvsUnLz4DUKi/\n7Qz+9LDpYUbAuUX0/ZUWkVu7x7nS5a73O6WTTd5q1MY6X8RFmpDJclQaomyyna0XUHm9Z5dSXCE1\nA3KOGTQXem2i3h/SMGGemypdUJUzZmjG6rScIHUM8Si4uiqfY8HY1b5vr1+/xNOn79XfzThW8ZAf\nfP+flbdeyI4s6FRYYzSiM3mvjZFBG04y9WJ1b0gjxOS9RgcBQeJWtN2VxGbrNRtY1zoAABwGSURB\nVLu8cxYv7irV4WZSFbvIgKt0C3iDVvM/GdIMpas9HXOlXL4+LeBabM4xKz0xxqTXEGLGuUZ5T2vv\nWWcI2DUlOuoZzkTAoWZjQ2JV0233DCiiDn5DV1vqdzGL0jCcs9jXQu/d1Yxz7T9VMzQXs2O5rprd\nYu7ZXC+aySBjECrLwDuv7AMr0AzTYd5hahRU2zM5ZC1izbpkZhXRmYxF0CwjofeP9EpN2cbSzEZ4\nyFc6baPUxnWB1RBr3mRXNmQgzp3qzYwmF5qIYJvomXNKcRMh+MN1/cyu4phE9HOttVpOYDYU8MPh\nCeapK9RlW4UOUuzUo3LPL2ZHEcG5Cs8w503mKetaKkTa59OkpD01ve1UMe+cimcYEaT12L5AhW32\n/plG24mAcCzvcdOknwM/I9Vent57pViaaVIanfXl/U3tdj2dOsVNulgWmPs6IrLh9uWeIUlJSx5S\nTl2kSgSuNg6X07Fn7ZdF1ctzWDWDSsww9Xnw3mv5w/Mf/LY+DyKMVLMk6eVz4IJ2JKKu8rt5Xuap\n940j9JILYwxCbEJTUVVSJz+rkFIWRmy22O2w1P7YnLP2pY5rULV16z1yZQnsPvwWTi+ruI8khJbZ\n2yojSu0p7JritNP5vFU5BlivIcekvy+cVQGShWBMzQA4r4tmSgzUzGfgc++ZK9B9QsBKBTsfj7h6\nWmnlfsb53PqIZthq93neKc3u1YXtyCI4nlo/WihbIoogxjY3N1l/ETx/XphMz2722h3g6mrGqVL4\nrmcHF1vJiAWqsu785AotZ+GMwasX1Z+53mNp+9U8I98Vu+/mzrCxe9+FVWYHTgmpPv+nU0Db4ixn\nFSZZ1oBz3TePa8Trmup6vUYVU0nM+n5vCOQ6vfNYZX3Dtvd4GcHm55a16z6EoV6SIbGzVwjQLFCl\nhV5wXYXSvWmzz2/p4QTRn5MYSH3W5smCW8rRTNqTVcTovWERZQ95Z7V0ApANddOpqBAbrwJF5frr\nXPFOn2XyrjAr6hyO67rJJHfWlTCrcFTKrLZbE/esuDMboU1A3RkhtQULev9Pb3vpBQhz12Ha9A3v\n1N9ZRPfN92/2ONV7V5/Jy9kRgvPShaOUyh0yYl0/vfRyupkMTpVNudtkCtlbrJvSIdvWNjKg0Dp6\nWFDtDesscKq0vXn2etqa9jsVwcSmPM16rz4VWQsIIE1FPMa+llK/hpRZ6bghJpzqfEw5q629t9pN\nRAAVijNGNBOabC/zICJlOe680zOINbTJnPausde7HZ7clH32w/eeKFPu5eu7L7DI78SbUHN/CsAn\nAP57IvqjAP4WgD8P4CMR+V59z28D+OhHfVCIEZ/UjQ0gVTc8LickboqdfdELYcFUFbS8BbimuW9P\nJ6zVgZa09jqKGEB1A04pKa0ihVWdpBTPwEalrb3OYQcoTQB9gYiFkqGbaM56UCn02so3T6HXdua0\neU/sFKCQsLYJQRZwZdE/LwsWXQhWqPrfesap1nwkGCzNAbEWd3dl43LzpAdRP3m8elU3vRSx1kP8\ns9K4e8Kl7BgiXtfDkbUGk7anSQhNSQxQ2l7MrIdVQ70Fxu150QXtyhmd2N4ybGuRwIylfiaEdXKF\nmLGrXHxPnbazxFhaDQDw6E5sqUfpjZWRs6r1MVjp0GUTrbaIGUvstaPtu2dvYZpDbQhB2YauN5Le\ne0yhjO/aW60j9dbc86vbcmQN6T3azb43Pl9XcDv0F2fxYnZcY8AnLz7V+9yoJuewKjUYLEWZEOXg\nOW1U3ZrvcIprb5qNflg4h6i0JZLe0FwkIXE9GBLpRms26tmuyK3WzwRMa/wdytc2aopIr6nOIujs\n7h7wMAA0OgEC5zrnU8Ta5tQ069rBIliVRmu1ZdC6RFVr3F1da1uhmIJuwIdDRo0vwftZn6tlXbHU\nINx7Ty89H1d89oPadoRF17fT3S2kHTByVjvGcIJtLS0MKYXn7uVL3XS86WUL+XyCr+1LUkpKWTUQ\npbKGFNWpEBx7GEDvO2BjP5RyVR1uduR8P1igTCLp1E0CNMhhAKUkUY5YW8CDSGt64hrAbePMSR0s\nDgHSKHHG6JxluQX7Jr3fyz+8CNBqR0934Fr+sLu+Bi5ox5QTbo9lfyx15XUerUddS72btOWQiGCq\nQQ82ToOfOSVVpCdj9NAdwqLP7LTfYX9V9g2IaPmGnya1UTgdMVdFWV7O2pqKzyuufZsfBpxFD+d+\nNyPX3z8lgW21yMZ0uiDzxqmiTQkDa90+uYRGeozrqs6W5KjPKKTXRAlZrHXPzTHg5fOyrmXuATPv\nJ71H1jl99j786GPggnaMIeJ1pc4SgLYohRB13xBmdUBziDBzscuSMs718LM7r5jqRNhTaecCAAdr\nkK+KX7SsUX0NEkauB8vja1aqpDG3/eAfrFJrTfZawys5w0Cw1vHlxEqRdfVeA+UALZsDdAteQrSk\nH4aMtiHauX7wsgT1c9zmgEOms/L1nqEprLb53ltUkbW6T5RaulYGYIBLzscQcVftaDcDi2vU8iIr\nosFtSNabEJi1y4PZtHIjb7RMxIkg13mUUgZCa1fW1WrT2qm5In2fSclAmrxBtvoeTiXIk2uChTft\nUvoxEfXZ6zRQDRBSd6ac6VRqa0ipvNYR6rBxM1u15c4aPD+1gzuUsssiWKV/ftujremlU5xyp4OW\nDfRy+2NMuKv6GmZDCl9CxNzqq4mwtPIsztoe0kC65sPSzybJkO5LVhKSa8FMwamqW4Oll9mxaFlY\nXAKmengXb5FNCzj2vTKtqZRC1RaXOfXAAYlsSl/6dWYWvYfbcjDvTK/VR6fBRmt0fASPyI2m2+s/\nJ++wxu7TJw2+l9YuAPDk5qCBzJQZp+rn7A4HvCnehJrrAPw0gL8kIn8MwBGfS4dLWS1+KCGYiP4c\nEf0KEf3K8cJiKwNvBcKF7NiiugPfCC5mx9PdZUU6Bt4KF7Pj+XxZ8aOBt8LF7PjyxWXFjwbeChez\nY7h4P8uBt8Dl1tXh53yTuJy/GuIPe8vAI8KbZER/C8BvicjfrH//n1AeiO8T0cci8j0i+hjAD37Y\nL4vILwD4BQD4ie98R57e1D5pJDjWKLa8IlCtoL29faXqklt6J5NBbhmb9azKcpx6LytyFqmere9i\nxvFliVw/3e97s/rbO+0X+vRwhblmE9PpvHl9pzRYDgs4Jfip9XHbRF5Tj+KfU8+khcxKlXi9RCw1\n2nyKCbnecvZXCJVeE3MG1+hvZsGyFEptjAEZjda2x/GT7wIAdlc38LsSab7Gk54ddQ439f5aZ5Vq\n8KT0DAyXsuOzp9dyqNmjq/0OL2oK/vbuhLlFRjYRLtrYMaNHiWJm7GvmM4h21NLMTXmP4LZGYZbY\nlc0iM3x9ZmZnsavKuiwA1d5QO9uLsVONpDd7ZRilViZmoEY118yqIHe7JM2Iigj2U6MrGEwtkoce\nCZYMfFCbPpuVtAl4ZsadRu7ReyGWq9XXWwNichZc6dw5dkW8Ske9mB1/3x/4jlxXyqEzVvt/xhDx\nwXtF3RobYZtlXe41nW4iH2tK8E19WgShZsESS6dPG4NUo48LZ1Uvhoi2yhPOyK0XL7Mqp+aNuBG1\npsqNoYmuZEeAZnIzRG26pRclAKFKNGZrey8s58D1Ou08K4tht5806k/WdWoOSNcvs6G9FqpM+d3j\n+YSpUaRiVOrVzfUT4IJ2/LGPviX7akc/TViqcBGvK3a1tx7FqII7eTMfiUizYbKuGrUFGc1m+s2c\nXUNEiOXANNveTw4iMC37ajtVmVPuDP3yj+UvOZcxtCyqsdrzk5hV7ZVjVFp7ydRVtswaVLQmxoi1\nZmbPzKVZOwqVumUfLBlVBDYimvWzuz3C+rx8/DSDY/ne3c0NUgucLguo9SjMSZUxq4DOxez4r/6R\nf0UmX75nnnbwrY+mMG4Ope9rK2cp190FnkpP7GajVUXWYgwINaoOQu8vaoz+fLq91c+8ffVaadvH\n151ifRZAam9OyRlJOoXMoke0cyQV9xIQfFOcTILJNoaC0V6jIYmqa6bMyDWTFmhD5RUg1ixNBjTr\nBzCi9n1lpPoMxLQdn+vZHud03dntr7C/KuN5VpT8L2bHpzcH2Vdlae8sTnWfTxJgmkiJJFXKp3rt\nAPD6uKhI09kQbqpwEbEgVv/H7b1mVpeYEE89+xhadv8cNMs4eQtT99kYgwrQzN4oM0BiQpIuqJZQ\nsjLtu11NgYWYVbQvJNa56QwwUxMtJGUozaarcjsYrDUTdJLOanGfU1htzxIZKCuAiGDaem5MV5O1\nRoV2qhTVxez47acH2R+Kf7ibZ6xVLVuWoH3pTcq9U8Ams5hi1h6oDkWwCCj+nafG9HGg2K6DsdT1\nabIE1r7yogwDa0kZfDln2CZeY0gVeoml6P9thC2l9fZEp8JuRZ6W0IUYDUGVci31jHRO/czHXNZ+\nAFitQdVCA009r5UZWtaURe6t4Y32OTuL3DK5mTU7Wfkwl5uP7z+VqzofZ++wVNo8UlaGCDGrr5dE\nENr8Itb7NxOpunPkUpbQXm/lVokFeanlS4Cq6eeYuxK/JRVujJwxNwEq6lTtouHY2WVJoGy+lDq9\nOSbWteP2HHBeW6mCKB3XmM4m2P5MlNXnnDIr08QZg9Payz+iijhuhawsnl6XuXG9m1VM7u54Vn+r\nMcneBD/yICoiv01Ev0lE/5KI/DqAPwng79c/PwfgL9b//40f9VlkDZbaImJdF1W4XcOq9XHqoKLU\npDVaDbMgVLpRDEG53ZyiUn7yPOFQnRkvUaXaTY4wbeGSjMPUnfxjKoe4hQTX1SgcCbm2e+FK52wb\n+zlBKa/7/UHrUKdpRlLe6IJATS2QsGzUxFp9auIzwqYWduW2AZDWs3rqNaLCSVvTnE+vsVbn8frm\nqcpIl3qsSm/1ky6Qr15+Vj4auIwdjcGx8vlDzEp7WEPUBZSIOk0V6Is1cE/2+dQO7wRcuSZDb+Aa\nrQi9ITYLdcp0ZqWJbidIyKz3e80Mvw2YCXSjTiJIjSo2eXBotUkW1lebOsKSOoXvUIserndO61aN\npa5+LILj2tqekF5nyKwKd4lFKSsGfaPdTxZX9fMjGXzWNnsyiE01uPz/cnYk0sOnASE2GqWQ1oLu\n/IQ1l0CDc07nLCD6nJb60tZg3Og8mHc7VRhdU1aFyxwjkrZ1EZhGnQOpM12aYFeqDpW6tGKHQpFu\ntJiwccAn53sNIQjSNnBhfT+j1GoBhcbEbQVkAdUNIa8rpAaelpjUkWLOurHA3Gosdg2LbvzLsqhN\nr3YH7HfNIZ30YFHn/cXsCGj9MOK6INYMqRVWJe/JWi1nsMb0OtIkWitIOSHH1h4JvVWKn2DaYTUn\nrbMT5+7XEG3qtloQgTYtK5BTl7AnoLR5aaURK6SOtdXtAtXp1ClM6tBkZoT6/EnOCPXAlIW0fYtk\n7o6AdBqxJdIaY0f9mZPTEbnuGc5PQF2rvbO9XcM0gyrlqCoiX8yO1lq4+v05p76hW9/XT0ddQwHS\nW3NJ31tYsr5OBMR6uDPe41SDluvxrtf+OANbleGfvGehkqSGcHxd7tNMVtezJUacVbshg0DaYgqx\n09rs5OFdo1O6fuhJvWG6sCCmtnZnrE2BE90xMgDOjaa7oRHvvMXSDiGStb62qEq2u9pVl682z1WM\nUQ+uNYB8wXW1a2HEtQRNAAApK41zW15ARIip74mNcp6ItLbw1hg8m51+PrU9P2UtTTgHtVxVYK53\ngAW+fo4zBFNtEn1Wu1kixMza2g4s8K3FzrblmOlOt7VGqYFRiiYDUNo9tdcN+p492U7NTabvg+WI\n0wIq3XknFNVdoIjFtkOe931X9663qak1axfdH5tqsaSE1NZP7oEsYwyo0RuJtP6WE7TVCqMfIjhm\nTaiIQDUyvIEeyozt9agl59dbJakeAkGTN5JZ7zHVQHfmFvQRVVtt+2EbdzsYTb7TaMtBtN5ba/R3\nYaH7XYljNhsJYvWREvc2MOWZLL+wJsbcglAbjYzd5MA17CBC6s/VvfSidlRtmpjQMqQe3S7W2tL3\nCSWwrAdwFrh2C6gfPgXorYUI+gyU/oStflZ6oJVK8Km8xcK02nZLvUwEgv3UAv2lU0PzKwq9tnxF\nNAZNMNlaCzfXJErqdaFEgpt9XdOvJqX1zpPtfkvMaouYuqIysGkbBQK3IFbKSvGdndW1wwDY1UPn\nYX+Fp7WFZNw8bz8Kb9pH9D8G8NeIaALwjwH82fr9/yMR/QcA/imAP/XG3zrwTWHY8d3AsOO7gWHH\ndwPDju8Ghh3fDQw7vhsYdvw9gjc6iIrIrwL413/IP/3Jt/kyawyun5TTsjtbpeHscaXZEu+cRrRT\nDBphyrn3erTGaoYDbtL303SFlapCLQhTjficMzQzs/NWox2nGPVzDpNTum+KSauATaWHNKGRl0Ew\n14wAGdJeOWtOGtWLKSot4S5ErC1aIqINoBN6f0YIq5gNbxR6BQxfKZrWe+yroIExBHElKv/RR9/Z\nKCEmPKmqufO8U8ru+diijZexo3cOH39UasSZWYU0limoGIS3Ft//5JNyP2LqVECUInGgUJubLWAI\n6yb41ihdzNDCbCLqvUYNwTc2KIn2nMwCFb84Z9YooCGCM1C6TJZSiA0A1nicaqY+scBWmu+Tw4Sp\n/uwtKTV3dgZLbGOC0oqIBVTD0yTQsVpDSqUIWXR8joCbRm3yrheUG4NcOSAnBkKLcqmq66Xmo8W3\nGwUXhLVlmERwVSnrV7s9zpWCmmLAYV+p7LmrCCfO2t/VWaPKfvt5gkHJLFrnlbpunC9N7QE4MuC1\ni19QD+l1GpIATD0USehKlFnKdbTv2HT21ki5JC50VBSKf2MWiAjQqI5ktL9wShG2vm79DKnvj2FV\nymiKsVNcmHFdo4AfvPeh0sGd6wIyk5+1B2mPdl9uXf2wzkcCsFQlW0uEQ83IztaCa9aQY4CrdmRm\nSL0mxACp2TayTjOIvN8j176UkZQVjUCd5mOd14hqYoZva6S1Gq0v46vzAwQIq1J2ilnXkdJftM3/\n3lOPY9xEqjstMAtU5RmZtYwip9R7bhJUuEhSUuYNWasZXo4EqpHdm/feVyqvhFXFnYwh5KrEq2yV\nC9nRkMWT62f1MrJG640x9/q19p6L3Om7nNUwgi0zAEoz9366R3WMNcNjjEGjBC/LGacqxjTvCJJb\nxsypQrKkA451DY85wRtCqJntCd2pmMFoiioWoqJkReajR991D0DPpDH39ydmnf9ZSBWSiwBrzwTt\nmo2w6ZHovNJN591On42Uko6gsQMuNh+tw3tPy3ogzDhTmXfis9Lzp2nC7YsqTMVdjRsoND7gvshL\nAiD19RgypO4/Ysy9LE2DMUapm4mlK9Vbo/d1iQxb+4s6WxSDVFVVBK713HZWVVUDQ8ufDBGuK7ts\n7/razQJVjLeb5zCzKO1zyr1ndNoI52SIrp8Gfa0hstpLepq6kBiIdG6067+YHZ3Fh9/+VvkaEay1\nPyJlxq6uH5NzOD8v1H4wa1YTkN4Hd7uvGdMoxMVvqP6CTdAM0wpNpFVGSM8WN0FDsqYlZcEMZXWQ\nMbX7fMs2k/oN5Dxy3nxWU0tHwGHX5jmg4k9ms9hDqwBKOU397sl57TYROfd1n6V3U8iMfaOJ+knL\nZ7y14MrEKNJsjfl22XXVWYuPPvygfD+ApqlgILiuLInD5HH3WWlHKsxdQE1E71ki6EOWCaoOC0Pg\nRrk3VlWseTMjjXO6zgmo9CEvNwE1eY1zZuRzZ6lwhqZdnSOY5osaB6ZK7iWHfb0GIsL1VWXCACrm\nuZt6eYK3pvfd3WTFg+1CTDFn7Kfe/aGJL1rT1XTnyWNu/yCsTCfvLK6KiB9ie4jfACQiP/pdFwIR\nfYJSdPzpg33pm+FDvPtj+gMi8q1LfNCw41th2PHtMez4FiCiW5RG348Nw45vgWHHt8Kw49tj2PEt\nMOz4Vhh2fDs8RhsC35Ad35SaexGIyLeI6FdE5IdFOb4xjDG9HYYd3xyPcUwNw45vjsc4pg1+/TGO\n7THes8c4pg2GHd8Qj3FMGww7viEe45g2GHZ8QzzGMW3w6Oz4WO/XNzWuN8+dDgwMDAwMDAwMDAwM\nDAxcAOMgOjAwMDAwMDAwMDAwMPCg+CYOor/wDXznj8IY09vjMY5vjOnt8RjHN8b0dnisY3uM43qM\nY2p4rGN7jON6jGNqeKxje4zjeoxjanisY3uM43qMY2p4jGN7jGMCvqFxPahY0cDAwMDAwMDAwMDA\nwMDAoOYODAwMDAwMDAwMDAwMPCjGQXRgYGBgYGBgYGBgYGDgQfFgB1Ei+reJ6NeJ6B8S0c8/1Pd+\nbgw/QUT/FxH9fSL6e0T05+vrf4GIvktEv1r//MwDj+s3iOjv1u/+lfra+0T0fxDRP6j/f+8hx/RF\nGHb80nENO77dGIYdvyaGHb90XMOObzeGYceviWHHLx3XsOPbjWHY8Wti2PFLx/V47Cgiv+t/AFgA\n/wjAHwQwAfjbAP7wQ3z358bxMYCfrj/fAPj/AfxhAH8BwH/60OPZjOs3AHz4udf+SwA/X3/+eQD/\nxTc1vmHHYcdhx2HHYcdhx2HHYcdhx2HHYcdhx0v+eaiM6L8B4B+KyD8WkQDgrwP42Qf6boWIfE9E\n/t/68y2AXwPw4w89jjfEzwL4xfrzLwL4d7/BsTQMO749hh2/AMOOXxvDjm+PYccvwLDj18aw49tj\n2PELMOz4tTHs+Pb4Ruz4UAfRHwfwm5u//xa+YUMQ0U8C+GMA/mZ96T8ior9DRH/lG6AVCID/nYj+\nFhH9ufraRyLyvfrzbwP46IHH9MMw7PjlGHb8ihh2/EoYdvxyDDt+RQw7fiUMO345hh2/IoYdvxKG\nHb8cj8aOvyfFiojoGsD/DOA/EZHXAP4SgH8BwL8G4HsA/qsHHtIfF5GfBvDvAPgPiehPbP9RSp58\n9Nn5HIYd3w0MO74bGHZ8NzDs+G5g2PHdwLDju4Fhxy/GQx1EvwvgJzZ//331tQcHEXmUh+Gvicj/\nAgAi8n0RySLCAP5blJT+g0FEvlv//wMA/2v9/u8T0cd1zB8D+MFDjukLMOz4JRh2fHsMO34tDDt+\nCYYd3x7Djl8Lw45fgmHHt8ew49fCsOOX4DHZ8aEOov8PgD9ERD9FRBOAPw3glx7ouxVERAD+MoBf\nE5H/evP6x5u3/XsA/r8HHNOBiG7azwD+rfr9vwTg5+rbfg7A33ioMX0Jhh2/eEzDjm+JYcevjWHH\nLx7TsONbYtjxa2PY8YvHNOz4lhh2/NoYdvziMT0uO8rDKTT9DIpa1D8C8J8/1Pd+bgx/HCXV/HcA\n/Gr98zMA/iqAv1tf/yUAHz/gmP4giprX3wbw99q9AfABgF8G8A8A/J8A3v8m7tmw47DjsOOw47Dj\nsOOw47DjsOOw47DjsOOl/1D98oGBgYGBgYGBgYGBgYGBB8HvSbGigYGBgYGBgYGBgYGBgW8O4yA6\nMDAwMDAwMDAwMDAw8KAYB9GBgYGBgYGBgYGBgYGBB8U4iA4MDAwMDAwMDAwMDAw8KMZBdGBgYGBg\nYGBgYGBgYOBBMQ6iAwMDAwMDAwMDAwMDAw+KcRAdGBgYGBgYGBgYGBgYeFD8c3iNuQY9y0+TAAAA\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x1152 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JNpc2MKAKRah9aWUkRhBCBE+1MQUy1HdVTMcVLaVl6uuI6kIymbr5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4fGQL\n1xnDgdSC9ntp3tnGJcfaOpXYIBsHVhzCWV3hCM5T0etRRDbyQ4VqHGIOzFTLqOvx5AJH6/UyStlp\nixxGUn8U5tTRAAAgAElEQVQ8qzzDYay99yxF9vMsS9Ay32UL79jdsNs4hiIk8e7377b/XA45d1t3\n7rTWVud479jZDXzf3SCnMQYV9zhsOpDUdc5Ayh661+cdWK/JshSAQClMp75Q0bI2a9VKiBmt2/2x\nsVF9BGubdGgJ9xvHWcvouljM2+Cv8sS6vbsPol+uHTUk23mfns85m+aah1T7TKOpZuE5FrMaOwht\n2+8XGIGil66hiedWrfCybngLUjaI1wklilWt46vQZImNvOUf0Fqn9TKM/bAO13Wd5hAcDkJ0IdDa\nd2DwWqWtv67KNJeNNkkWTDlPLQG7jfWTsBX2hsY36T6yfp+5rKuX71wn968CkFcLzOlwT/3BMlb2\njHk9ZWsvwEqn29v4SViDUO39H4V52iBXsygx91gCDsvkmLT2WOcwkYuk8ZgYsOv3kmxNtqhwdSy8\nzBITsO+NyDcDBLTp9VBVCFjMXtth78WLANx86RXu3A6sw01dkwk809YN3rkkgXL7zp3EOZJrQ7mI\n8GWNMdG3sRg5nFSuSUzBSuvOa58OlvVslpI8xbCHFUj09sEey6dCMNebLMnGaNdKBnXh911pKa0U\nsffc3YHFo7B7SbbQhY+qVqvHw+p6SHhs7R7wzG98HoBBBn/qz/4JAA7mVVonR+PW8+j6l1q1JQwX\nL73GX/9bfxmA//0f/H2s+Jury1lb3qIhz2VOaB38riRlY+nL/yrrkq+j8izddlWReFkqfDq4qqyt\n0VVa8fxvhnLHU+fXeOLRJwH47DOfak/rXh1aVrsByPZNuS61YVx32vbVtH36ZnYMzT22Yzu2Yzu2\nYzu2Yzu2Yzu2Yzu2r6h9haG5bTjC02Y2aksi+lGuZE8IKV595bUUHVRZwc5uyC5kHvZFZwldUEhm\nJvOOwgjEValUVL+o5hgtUIdSJ8Y518uxUaQ7K+gJVMFgEuSkNy4w9TJ1JZDf3JEbiSq5BXWMBA8G\nibVQ147ZPETpSluj6hD92L2xlTKlfpQx34tELm2U6GAyQdmYPdQsCXtX41XS7Mu0xkRuDueSCPgb\nkjEcNTmKbrWPwB36+hhUyQaeXl/uq84pI7umG9A0AgOpSuYzIf1BJeZR5z0m6aFqrAzTejbDCM2G\nKnogkD20aaEfzQItGki5t7gIyfaWTJsU4bSNo56Fdp7vVwjiBe8H5BKdVsOMWkgndmczqnjf1iTo\nYJF7qroTwVPdv52GuZtJh/h7bbS88yYpha8UCyF4eOnFS/f8ji/XuhFfZW2CjQ0GOWdOB+bX9aVx\ngt0dzCpKQQb08kGKir768mvs74YGrEuXWAyrumFfsoazqmIu4bG8sjhhOFTOomTeZZlpNTu9T6QF\n3rtWizLScMRMgfKJJdU41YphqzbKFj4Tx5+CyOrZtKzBRnkWgr5omrKjQemSkDyoNO98A7P9cL1b\n8UhwGaUdvV5ol+XlMSdPhcj29tYeVRUJ0I4amqtYWgrZwV6mMbJ+6GZBLpn1vCrIBfJWZAYtkVOF\nTRnv4XBAIXBcpxVlIgMiZUFxnoVktqeLSYJ2V43D6bHcj05Zq36RUwgyYjwaMhAN156G8SBL7RxK\nJgQinGdYyfCWi5JKxn/ZWA6mwly9u09jQlZkb2+PMw/GDJ2hph0DXajn3W3W/Rue0wXIEQGVoGjn\nZsowdr7jqHOkIVnw5a3V3vs0d5zWgdwOMNWE+UEoI1BFjk2QZ9OuMb7BEctKWvbiIi9S1qTXyzHS\nxN4ofCGasXLjMSqvnU2s41qZJGRfuhIXM3p6QROF3muV9AYdPrGXZ9qxENbcyWKeSLe0azkyrCYx\necY2iHY/2rD3+tyRmIImMmdai8raspF07zh8LpDdwYDRUiCkGeSjBIsrBjkqQtubKq3DWueJiddk\nfQxCtOdUIgxTqomygEFPOMJmFcQWzKwhi2zzzmOaijwSEeUemwvs2zmytJrmNCqi2iCCdLTpUdZt\nxsZFmGWRow/CnB0XFT0V9lNVZ4mls8Fj85CBmpY73NgNUPK1/BZLsscsr84x4gtRVSD+32xrn4Vk\nRIvB0fo5HoURx0DPDhIyxtMSsDmKDqipVah0KKJmuXWGG7cD8quyukW2LeaoqWRHTR87lLV6uaDa\nC7qyNy5e4fargV341Rde5PbloDevSscwC+iV9bUNGiEXNEXB7u4eX/zsF8Lv1TWTSQtZns7Dvrs3\nmbYoF68SSseg8S4ijDLqOs67PGUM68azEL9o2B9RliHLbUzBhpAk9dZXyOwsPWdkdtVKkcUSFRx9\nyRQrk9PMw/XVEWe2IfGFBTesg97oumtWoKzeeh57/HEAbly7xNe97z0AnDx1Is3lSy+8xC/8s38M\nwLd+89dz9pHAvr+8tpZ80a2tO3z8478GwM/93M/yhWdCnwxyzbgX+q5nFLkgJob9nF7kmDMqKHGI\nT9y4hkx8WVs1TAXBUjYeWwtMP/McVGFdNrZMkH1rFXjJtmuLESTM5z/xGd7/4W8N7WOKVv1Ba3xa\n7El7YtuAAW7dElD51r/9MpfSr+xB1JPY3LqsmN63tOb7uzv8m48GltlXXnk1SQvs7+/Ri/jpvKCJ\nrFFVyXgQGn/Uy+nFhbtcUAmFellWbKwEHHtmCg5k8Z3vldy8HWoT9qZTzDA4P0Zp1ntRoH6V5fGm\nUErDrJoyk030pUs3ee2GiNfv7tNU4f1hf8jKUrinlfUxRjbtg3lFFSUU+gXz62FxOtjbRUe4sPIs\nC2j84QcfwAqsQg1GaUA41R4grHUYGTRa6xai9lsIJVO0iDulXufjAVDXFVVl0kU9OZxMDxruLMJz\n26pkKofB0bDPdC6OfdMyAZbkSVp3Ujbc2g6L6rVr12l0rBfrs74aWM6KQjPoK3lfk6Wbc1RVxUQc\n2bJ0HFThuqu15c4ktPNkUXIg9zFdVExijd+sZPtA6iIWFuVk4VeeqJ7zulK1t7UvxsWSNkghbXWU\nZut4IPFpAV1eGnLyZNhQirxI/bK3v4/JwljOi4zd/QD5m7w0x74oIsk6j+c8vNEU/Sh8n3H9Zpgr\nL+5uM+qF94fDHgjMOc8zNtcCJPiBM6dYEsF3UKQSTO3xymMTrJ9WSsJ3WD69Z38vQJpu37lNLYez\nxlqyuMhiGMiBJzM5K8LK3TQ1S0sr8v0502kYG875VP9WLUpu3wzOwrkHNpIIO4rkhOV5zoaw7y2v\njNnZ3pNrjnZuKhSrg+CkbYzH9E4GMflC1SzmwUmYVyWqlmBav6CIzIi2oh8PiuNxclhDKY84IcZg\nBaK1f7DPvsyD6cLihYFcZwUWCews5sxEsmE+neDkwHPu3FnOnQ01RCdWVhn1xxRSC+U1ePl85T0z\n+czVO1tcvh7GzWuXr3Lxamjzg3nN8nr4jSdeepHz7w4lDL3RGCe4SK07bI2H2ot7L1q0HAbeuSTb\nY20LE22aJkHMq/poWTrvx77kQTV5VR24ezlnVMeDvKOZScDXmyTI0mjYkwPI1YuXQeb40tIqvTy8\nHvRyTkiN/NrqEmMdD5sKbVQr82JrKtHx2a9UYkW/fWeb23dCPy6qKYtpWDvyLGcs865y7QFaa42X\nGtF6d5e+PEPtPWWqbf2tLDn58i0wy0ZoqiNPrMA61djPmorzq2GN3Ty5kSDx3tq0Np5YXWEkchgm\n7zOZh/a4vLfNtAoBnb3Jgt39sCfe2r5J7cPc1MahJgI/14bxUmjj0dIy61J28eCpU1yQ+sNsuA6z\nBSYy7euMUjb5ed2wmIRDwu2tLa7fCpDQSV1jpd6xrBq8BDPy/gAfWdG1ZSprQZmPmS+kvKUqSIVu\ntka5uHlaIlJ74RRTuZ+8rshFHcE6Eh65MZodG9bncutoIZ1KKXysoa3mh0rJ2iOnpptcSRBFbdK+\nVGjNgeyVRoOL5Qy7dygEmmv6BbF098rzz/DpX/l1AK6/fJnbUuO9P5skhlTjDRvj0Hcz71gehfV/\nNBpRmJz5XmjnuqnZkn2qtA0Hk/D+3nSWoPI6y1FZlG8qMDqMv0F/BBIssI0jNy0cPzZFVVXkslYX\nMTEAZEWR2LW9850jX7cG2aSDXekb8nGYA6cfOw3/8Yv36JEv0xSJLyVIT6XHQPkIDTa4hExVPPrI\nuwH4fT/yo/zKf/iPADz51OM89/yzAPzZP/2T3Hg5qF6UNz/IVA6DB2XD3jTMx9euXeHqjVByN5lO\nkfg03kMdg3VZRtYXXpdhkSRhijzDOUscWwOVU8jhdVqWKRDgy7otJ8lhKHNnqCAfhH6czg1eeFm2\np/tYwnxprGEyDWOr19NUsQ7At/wy4YDZwv1dp9b3cDHI27NjaO6xHduxHduxHduxHduxHduxHdux\nfUXtK68jGln+OuxtmW6L5htXs7YRYIFF8So+MsgOhjSiY9Qr+hRS2I2zuDqc8Au9jI4suLqfdHey\nos9MBIJn2/tsLcI97NzZY3dbmOumE3alKBwND26EomvnzqHPeJaXHgjXTaZs7QZI2Csvv8TNnQAX\nnlUN/TxkV3YmU+b7IRqxfbDPcBiiynOrcPLMw8Equ7shizSfTCgFyjvKFe99IqT510YF5X74/sFg\nkArKg9C4FKc3TSIAcc4lpsGmaX7LsqJKKzIhDXK2C4eiEymzlAKp09qlLNR8XrIvQvSusYkJT9cN\nPpK5aJWgHF4bamG0Lb3n0o2QwaZx7M7C99y6s8eiCqGg6cEOpzdDJu1D3/F+HhJNx9w4prMZt7ZC\nRPDGnRm//oUg+Hvx+m3mEmFyTiVW35XVNQrJZje2SRnPcl4nRrxcmcRO5pqmZWf1cBTgvS6BT5mi\nUUdjSoFOUDiV0tzrq6ssSYTVe82BkA/dvn2bzRMh27a2tkIhxfrTcp4itdaTIqHOOZaWA2R0cnDA\n9CDMr3c99CCbkikc9PuJMGlvd5dnnw8wpBs37vDE44H5bzQapvGGUqjMpOxP41yHqMqxL9mBq1ev\nJ7T2ysoaK6Jl56xlT6LFly9fIYqRX7jwMGcERjsa9Hni3eG3HYYvPv0cAGVVJ0Ktuqm5JgRjDz/8\nAKYXMg5ZbjpyoYrxOKwJmydWOTgIUfHaHnE/4lkbhjH/2LkHGT4Y1qpxoZgchPXjxs4dbt4J0U+f\nwO6BRKYfSxu0ackzOuQ03tpEOqCzjKXVkFFZK1boDQOkbry0Rk9gt8417O5IFH9vN2VHh/0eY0F7\nZFlg6MySrqVlIdBl6x17wsp4+cYtLr4W2nm6KFlZD1mk9d4IBJpW2opKmElz7TGRaVC14umvL1t4\n/dwsy5Lr1wP0bT6f0ki5SF3XHQRKmx3tkrv8VtubaZ12yfa80hQyl8u9CY9uhjk7G9TMBH5OphLD\n+tzaxHRd9PqcPxnG0nQ25cp+aI/d7S1GQpZx9tQmTz72KAAPnDkNvSxBcOuyZCLwv+devsgrV+Xz\n0xlDYd9eXR6T98N8uXb9BvPLIVN64eF3JVIXR40TLMzt557jP10LWbjHvuX9jIUQxXjVwrDfhn25\ncOg3Mu9b0it9CPnlUyZk1jTkggZYX1mjJ3DEqqo4EDjkjcsTXrsa9qgXLl7mxUtXw/t3tnj0qaCn\nfeXyZR5+IKxbp1YGbC6FObg2GhKpsRtnuXUzzKGDV15m7yCskfPJLOnGnnvsMT7w9R9g92roi53F\nHncks3Pn5g1m+7J2NCVKIJq94QgEFuwazzSWsRwccCCZxNv7u8xF1eDsw++lWH4IgLLUGMmCal91\nmIXLNDMnleNOKSU6ZcNAS/bVOQ7EV5hmhqmQw+3uH7GOKK12pq0qdGIL9x2dVn33h8Ifr6jlmSpv\n2VgX5tvc4MrgtxjlcKKP2xvl7Ije7yuf/gJeSl2yCk6NQ//+f+y9V6yu2Xnf91vrrV/bfZ+9Tz/T\nG4fDYZcoSpQoy5QoW7JF2ZGdREEcOEZyYcBI7BgIEMNIQW5SruIYUeIACaJAMRwnQuRExaJkFmk4\n5Ayn19PLPrt+/a1r5WI973q/MxqKM9QeyqD3uiC/s+erqz7ref7l9Po5P39MXdOUjuuqZv/QxaRV\nXbG+skYlcWCdl175F2tZEoG3jY1ThIKEsVZTSGW7qComggCaZ1MiQUTUSnlIZw2Ugs4LrPGOCLaG\nbuN1G4UUzWG5UFVD6VZhOwyYi3dlvNLhgcdcFfKxTz16vBVRfGF3EWe28P9uz2zmNVh2D1yc+dpr\nr/H4Y48CcLh3h//gb/4NAF545vf4iz/lYK1f+uIPs7fv+uyFV9/m1r773f3kHJ3UffDt3V0K6dfZ\nPCcXtGZhakrZh422WIFYp0SkUez3VWWMV+nfH+ccjMVjucaLgWllETQ9G6sJ62sO7n/nIOPSU+43\nvHrrFeZTt+6WTi1x9Zar6oZh5eM/Y52YGAjlo+HbKb2gptsi6Jrn+Q7+HsLe7/tFtFHRjOKYSAL4\nOIy8ytrN23d58WXXORZIZLGkccikcJ0fhKGH5WSzKaNDN2nMdo8qEp5MEmEkIMlqw86OO8hMDZ2+\nC4LDTp9o023cK/0Bj3adGe/W2irJiguwTLZDUs6Ia7fYitmM21cdV++hc2e5cMFZauRolPBZjVEs\nCZ9zd7jPa2+5w6Sa5vTX3CGfj8YcHbrANAw0iQTap9aWSAX2dLRzgyVRmO10+0Bj7tzxwRb2XvPu\nRXPwxQPwOJti0aDY+smptfHy0UEQemxqXVtquWyUReUtWxSawBuoK8JGQQ3lea+1rallQ+90Ih58\nyGH3ldHcvOsOTZ1MuHbbbcS6aiXlb92+y/amC5p73ZTR8IjbO+55X/nWZd664l4f6ohEgtrV9VXO\nnjvr/h5FZPJe8XSKkrlYK8XeobtUZWWFEv6xMTN/ITMWnyBwa/N7G4PFsbPfgWf6PTdrKWVNKWsY\niHXH1qkNb4ZcVZbDQwcpPTg4YHXd9efySp8Pfcgprn3zW897bmFlrL+saYtX311aGrAj0Ny3r17n\nrqjJGmP8QRmGgeep3rqzz1ygyNvbpzh92m2q/X5fzKfdZxRVyXjiLsp37+57ldrZNGdlxb3G2JKj\nIxckBYEiity+s7F+muHQ/bbJZMzKsjuke93UQ6miuIOWDTdQeDhTNs+4e9dBnq5fv07SuwRAt9v1\nCp/WWg91XVtb5s4dScYcHb/NwGDJjd3DD95PVwL4cnLIpW13cXugPsPVW+5S8Pa1GwyHAhULW5XC\nsijoxG6/iXRAIpcCHcf0Qtc36BBDoyodowTGWRYzdkduj51NJ+TC96mrwodq5bzAhHLAqQqTKQKh\n7itl2JOxuHX3gInAwa3RbG+7PVYFIalYBeQ11AIhu3j2nKdnBOA5h+5934Ur+B1UcquqZmfH9dFk\nOqY5UY0x9+yl/vExJxTe2d4vl7iZd+jA26uYskCQXyT9DmWjeEyFEorJ9GjoIfFPfvgpluRSNCtL\nJoU7K2fjVUJJ+EbWsL/n5n4SabY2N0ik/0fzmQ+og3zClija95f79Hvu8VInIUzc47PnLvLaqw7u\nNjzYdxdbwFBRKTePzegIK3DrspxRNRSdKoCFi6iH5S+M1zsTEO926fxAEraLyqAL36tJXtRFyXTo\n9q3R4Zyh8GFfv3KNr77gknGvvn3H8/uiMGZVxmj79FkyGYtOL+Lf/jd+EYBHT23QleRvRwXMYklg\nlgVl1l5+Ghun4XjMt665y+2v/dbvcmV/xIvfdDy2yzvX2Trl1tpf/6Vf5KnHfhyA9X5CN3F7TRj2\n0bXb38r5nKEkn+4c7HNXEusvXbnKr/0//x8AOzdvcz4RqHFt0apRxs8J5VJq65pS1uTd4ZRZ6dZj\nrzskSWWvqWpy2bPyqqIQyoFOGyrH8TRlocoExljkxBIL2MB6jQin0t/sDa3aqKkNlcSoY1Oze+D2\n23qaUzXUjDDGJhIjodi/5eLYjuqyW7i1fDQpaBZzQkgi53I3CUjlO+zv7XJHzivikD5L3mplNDri\nQGxeSCL6vUZzxBB5FfWQsElAVjlLEufMqckloYDV1HLoVijCrntOEATe3iybTCjkUqSU8hSGYEHd\nWivlrfSIDP1Ntw/c9+H7GWy573ZndPuPHZf33RT+pqNo9VgWW61KGgEIHWi++byDRv/8T/wMTz7g\nHAH+7n/4N3np2a8D8MC5bT70yAMAbKz0OCdJ7AfuO8Mzz7tL9Fe+8TyXTrmi2pmNTXbErulg/4iD\nfTdeeTZzhxmQ1xmTUOLHTsrS6YGntZRlxZ7Q0g5HFUO5TKIjb2/W7wVIToa0F9Jbdv2pM8MsFtu0\nU5auxFilPfKUpThWBEaKgRYf85gS6ia/Y+9VwW12zcUzcfEiqt7Hvvp9tm9p/UKDhWx4oLXnkrzw\n4osciidSHHU8d2A2nvlLSxyExBJMKlMTN5mMqkCJhUBlDbFkf4xVnBY+RjmrePEVN9G/dfM2z11z\nl8rPffJTfPi8y/JG8ympcFPzac3o7l0QEvBwbw9Ty2iHa16A4ctf/SqvvPUmAI88+jgbcnF94okH\nefxhV+Hcu7XLTBb8fD7z+Zgkjr0dSBJorFwO8umYkWRmoqRLLXj2ZGCJpIphaHNyQdDKhzvhC+0f\nH2ez1voLl0It+GhqmnKQtdYnTAztRTQIAwkgXEIlFi6urWtWZeHEYchUqlZ5UZEm7vkP3Xee6cQt\n5j/4yjO8+LrrbxstEXZchr3fDYmiRmAj8QdDp9dFhRG37ko1++Ydcum50+e3qRpxpEDz8s0rAOwf\nHXhBI4UmkDmXFSXIIRoGNVaW0aDXYzwVwn3ZSuY7e7fvPdi5Z5Efa1NkUr1QGFalari2uuzHdDYv\n2dt3AcZkOvMxfFkUrEiS5Oknn2R52SVubty8xdXrNwCYVpVPpNx/3yVfKfzWc89xIEmYNO14PnBd\nVd5O6fzZs0xGLlArypIsd+P+8MOP0B8M2vGqa8+93N8fcuOGu+zu7R6Rzdz8yPOS5nYchpogdD/i\n9JlNHnnErfnV1WXGwhPWylXyAC5dWieROWosvvqdZTMmwrfZubvD1lkRaUgSJzKA7HeyNw2W+p4v\nOpZq33E1pZXn+HR6KfXIzUFra7oSnOki4+wpdzGfTicc7Lo9MK8hUC7AxdReIErTcm/jKCaR4FOH\nIRMRGNvZu+0tbOZZTiUXs93du5SSJT9zestz5IIAbOzmmI4UqgiJrBgLajDChx8fjohTMVcrDHfE\n+84YCIRrfjCacvEBl5Q6v32GiGYOlT6D+522vYX8PEq1Vlj9XtdzgEfDoQ8w66pqLxB17R+bquYD\nbWrxoXqX/9D+EpeYlAsampdeeB6A/de+RShcoSIrffV7nueEgdvDttYGvmTw1ssvEwkK5MXXXmcq\nF6SPfvhJHjzj+L0qm1MI1+/uzg69JPYaB7PpmJ09d34T9+nKGby3d8Qzf+hsEILa8qGH3JmobMm5\nLResWVt40ZS6AqQ6MEgTb12Q9juYxgv1A3B5+CBaw58NDCiphGz3B3SES/vs177Jjdtujldpl1D6\n//SpPg9+3CX7PvzQQzx00QXE60sDvvqyS25fvXmDJx6UxKkt2Mvcnnn17Vvs7rt9JgkDHr7onqPK\nGV3Z/1aSiM8K6uTWjR2eef4NfvkX/yIAX/7mV+mLONAv/OyXGN69DsD0YJ8be25/t7bL6RXRDKgn\nrAoJLuqFRA13/OI5nj3tEkk2SKjHLp7RSYCOhCdnS1TdJBpqL6IzmVkOZI+NkhTdJPqrmsZ/OIlT\nH+dE4fEnFExT7asKn/yvbOUFuaxRrXaHtY1jIEppSvk6n/zxz/PZL7lkwTwrCJdcn+lOHwlXwRpq\necHdOwd849suObM/zTmUWDLL557bvzHoc1YqXr04RgmveJTnrFYVyAXyYDJmIlXdsi64ve/in/k8\np5RYdH1jkyXZR9IgJBVP6iSIaQBvlampmj3QWorMPT9aMiz33NmwP9njSDRX0jhl7Pn5C8kYVfvk\n4+b5TU4/5uZldzNlZ+zmVTDpfJdRef+t4X8qFpJRqj3jAl1TC685QrO97ub+g/dv8/K3vgbAC1/5\nPbZEt2JjecCZM7Lu4i5a5t76Rp8nHnUX1OFowjPfegmAN155k2tHbi5PZ62IYGgtsZy5K70OWja1\npd4Sy/1lcrkF7h/uMZQqaDbFEfvBBSuNDWFuWBOUxeneCl0lVmz1hPLQndNrgw5HlYttjKppgGZB\npahlP9JWeQ2eyhoaf3ljF11zv3t7P0iTE47oSTtpJ+2knbSTdtJO2kk7aSftpJ2072v7vkNzjfD9\nrAnBNPBS6Iu65uc+98Nck0pXNa3QDd8qjukKvyhNY4zYeERBwuaSq8akVnsbmChNiEQRLrIaGtsU\nY3nqPpehO7O5wVMXXJZxdXnVw3+CpRXinssszA4UWVaSSPl8mpUcChdiVcd0RK3yI489zaVTLmO8\ntTYgEyiMQrMu3KlTjz3Et157273v0Yw1UcS7bK7Q6DQbo8jksybjjFAyTGEQEqgmBVxQ9N1vDuMO\nXanSaN1Wz4ypFyBBx5spNMYS2FZR02foFzMmtmxNrZXxUuYY60VDtQ5IpHo0ns05FFjRUm+Jnoxp\nMZmjS5flPb+2zYqonPYHfdbE9HltbZ1UeJ11OaeuGmsfTSQZxKIOmKkOu3P3GaMqZ0WqeNubPQ8j\n0FHMzbuSkR0n3m5nMp0QD0RddGWJnm44ryVTkadXVlGmjRn5nFo1fOiAWLK5ZVm+LyjYB6l+7Awf\nxGg7CllZddm+dCmhEl+EcTHh7shVHOfWWS0B3Lhynd19B8+7cOEiK5I9n/a6DCWjOhwfeYXYteUl\nLn7mEwBcPL3BV7/+DQC2zp5nc8utg53bt+lLdvWHPvVpbklltdOJGU9dJjfL56ytrzYFaeZVxfKS\nZP6625w9vSGvGfDaqw7iNhnP2BY7mstX3vZwt8efeJT773OVmTiJORxeAxw0p6EQHB4dMpeq8ebm\nJucE6n316mUKgccNR2MO913Fdm11HS0a7MoqtGQTu50+m8Kv3WvUc4+tKQ8BruqSTKpVy52QVL5L\npKqFCHUAACAASURBVDuUsjee3lplOHTZ9BvXrhEKFDMNLaF0bBTGXvJeB6CkIqVC7W2W0jhgY0Us\nFVgilrXZiTRF5vbIzc01lEAjOnHM9pob626UEGhNLoiDME3oCbxzdSVhWfbGRAfMhMePDbCS8a2K\nirBRSzbWqw2OswItVTWlW5U/pXSrYm1bfovWFt2gOOqMXNRcb1+7gmksf+raV0qrul6wgPogjNcX\nYMPeikN5jUKtonugno21kJuv0h/UPPP7vwXA1//Zr/OLP/OTAGyu9pntie2C0fQli795aoX9oZvL\noyPLyoaDxz6d9IjkOzx6/yUi6acyijiS83c2HFPMCuaB2wPH89Lz+0+vrNAV9e1TmxvsXXfra3V5\njfsuOhrM5bdeYanjfsPK0gZHwiOf55pOIYqaJqffk7M86KHqRsWxYLFs/L1yPY+bI4qi5a2HUArV\nJpyXfHzb8bf/xr/+Sx4tcu36FWZPCtR2dYASBEaSpGyvunWaAFbQYWknIVr5MAC/8RuH7F92KJBi\nukcmyv0m75DXri+TNCDuuPfMssLD66KlPsNcrCZu7/Hv/dLP89EPO57eR544y6/8L78GwK2rl0mk\nj6Zzy2t33L4/C2NWzju16suvvM5W133G2soSW2vubM1VyJZUl+47fZ4XBIY9qawgqMDkhrnwD9+p\nbRFIpd6qAl2168FDnytDJBSC0hw79AslEOPQlJiwUdTW7RxcKOVopbxPSFnVxHL23X/uDIMloZil\nobPrA4o4gKhRsa6JBEM/no25cctBpkn6rC27M0d3V+lIfGHyjL0dt1fZpWVCKTNG3SWWN7a49YY7\n+6ZVRS79bDOoGhhzPMBKcL03q9gTHm9QV/TH7jmba+vEsTtb4zhmJvGxMsbTpQpdUkusGx+NiUUP\nYHXjFJ2LrjJ49bWXvR3fYHvAx37yKfdZ3Ygb+w4JsHv7kFrW8vLq4LsMzPtsqqUEKiyqsY/RjsMN\nzu6xAZ7+0id/lL/9xb/k+uDWW8Shiz//i7/z7/O//R+/CkBWzjkrSI6kO/D2cjpKOHfR7VU/feo8\np865+0Xa/V0u7Lv3mWRz5qKcnM/mzASmu5x26Q5cfy+vrLI6WPc6K/lBQUe5uEX1FWuCbEm6qY+z\ny9pQCqVlNM6Je+JMkMbEh6Jczxa3e2693FZDKqHKRKoGQb9kNWiJC1WkPfTa2trhdnGsOw/NVdwD\n0lnE67zX9n22b7H+EK+CiqjxxdF42MP66nrrnakKVpcd5DLthuyLj+g8nxLKxWNjc5WBQDqLcgYN\nX6+ufdTc7/cxofBNtPZwobUw4ZJchqeTCY34tK4qJrccuT87PGKls0xHFuQg7TIdyWVyesTpZeFm\nXdwmfsAdMmU+pZGtGQ33GchvsEFILWX44WxGZ9Vt1qcuXOBo3104K10zlO9dj8Z+w02igCASCFmY\nkMj3STt9IhFuqk19D4Ts/XipvZ92j+SH5V4InPwjSSIG/USeX5PNpdRfZZQCQUnigJ4kF6aTqYe6\njcdDcoEYDZaWiRs5++WU81tukf/cn/0x9o4anH1JJZCn+WxMHLv3v3D+DGnkxrrMYToumYj3Yzdd\n4f5tB614ePMUnaSxwFFcFIGda90Oh2vue6hQM1h141gqOBIbmIPhjJvGHfhVPfbQt2le+ODVWuNt\nCcIwbKF9C/y0P41mrb8nMuj1WRcOUhJFnt+xv3/IkfSzMcqT7IejI1KBT8ZpipZg4PyFC8zEEubm\n/h6zuTuYdGC8r+zDD19i66xLIuwNRwSB64eVlYssi22KDgoef9LBZpW1HB715Ds4/lcTgIdRxNbA\nvVfSSckzmQfTnEeFwzHPchC+2eb2U6wI/3tV1h/AbDYnFwhTVRpmIno0HGY0k3p7e5sVSVDt9bpE\njfR8lnMkNj9FkRPHYtEUBF4MJEkSVsViaGtr67uMzPtr1lq6IooUxpHn9fQ31j2nSMUhh7NJ8wrO\nnnF71f7urv99OozQjZcayj9GBS2t0lh6YnPV7/b97yuKikCsqVa7PQ4PXOJgOhkRC1R+bWmZjthU\nBFZ44HI7LIqCVC6ZSZQwkjkXRxHnzmz7z6jF17K3tIwVbsytG1fZOO1+T9jpef5WZWp/+fzOdw1F\nLQf5aDTiUMTnbt684akFtampqmbN1pjGX9V+ENBc9UceOV5TG/E2+7nWbcIEwOrG5xB6csZNp3O6\n0mePnL2P4dRRUUaTQzqBBDM2ZEOsIJYf3sRIp3U3lv3+7PIcwh2d11RHkrxIO87WqPGXC0Lvd6fz\nEi0w8SUd8tNPfQyAMi8ZTdz4Xjy7ybKcE4GuvRVWXRd0xJf2zPopej2395qiJG4G9Tsca1rrd+X0\nfj9b0Kwp3UKm67rmM5/8JACn0w617KVPPfIogcA1r+ze9knszW6XpaBJhpSNrhoEhmU587sllLL3\nrC4lBH1JPG2t8qGOWzd1VfrEUBAHhLIGyzDi5ZccJzSMIx64eBElUNRLqxucElGyF15/lU9/2Hkp\nntla5tL5TwEwK2uMeEWmF09RSsIys4aOnKGDIOXJ+13i/5NPPs1YqEYv3LqJlflnCktmxCKrrv1i\nDcPQn5FFni9QjRSq8fGsQyqh/Rx3QkEp/Fq3xvg5bu09NHO/t2ilvZ1iXuQg5+D+S6/yf9926+6n\n/t1f5tLTri+jvsbIOhofHjCSy0my1uNnv/B5AOaHM3Z33Z6UdDtEEqcMpyMOxvLaakqv5xIWP/dX\n/wqXNk7xG/+ruzDdvHOHftxA86esSmzd7fY4JYlRheJQkqqT6ZipiHHm0wmxaJ0EsUY1/WwNiNVH\nrDVaLrGRyQhEUKoTKLrn3bl85c0X2b7g5uKf+cUvMI7c73zjzVfJ5WKzfzSjFI7izs61P35g3m9T\njgrSPPaDp0A+kkobzhjXTz+1cZHf+Xv/FQBJL+XsZ50w2Id+/DP82c//GAD/7Hd+h/U195vScMkj\nZVUUeUu43lrMn7vkEjtPf+Iz/PPfcMnBl156iZEkw6eTKcXAxQVpkng6wurKCt1OlyxwfbXcH7DZ\nCC5qTSpJ28Hy8oJIHf7SWJUWLcmJc6unufuWu7PsXLlN/HGxckoVWeM3rQPvOxwohWmSOralUxrl\nNCXAiZTWi4xRfys1/txU70Pj4ASae9JO2kk7aSftpJ20k3bSTtpJO2kn7fvavr9iRdb6SkupQ6Im\nwxWEjT8x/bTLpz/1aQCe/fozWCkXT3cPvHxTb2mZjmRqYw1a4L4Btc9uW2u9gAVl5aGXQRT6m38Y\n4knyUT+CShTSplOsyGEPwoiqqKkXFBIF+cXurWucWhG11dVlrMCV4iQgb+xHuqmHgc2zjJlUbLKi\nopSqaRJp+kKCzidHzER5LFYRpViUTA6P6ApssbOUEZhWydObsC9UnD9IawFFK/BhbZsNNwYaV51u\nL6EnBsVVVTEVZdOyNF5VNUlDZ3cBhKGikixMgKKWsa6qnK01l8VbX+kRS2Ur6UX0xDJjsRqD2USJ\namicpNSqETiA4cGI6VigaXGPuWQsR+WMDbEEWO6lbCuXobpw8RS1KPzqhWzr7tGUt+64+VFmlRfO\nisKISuZZHAQUYimjgnevfmqt/5SrohbdwGF6A1Yac3OtmUjFd2/vgJk8juKUQEj2m5tbvPCyy6aP\n5wVPPuayvGnSIZd+LaqaQxElquvam4DHacApyQIO1gaUYkiONcRi85NEEYHAgxXaWz3VdY3W2u8j\nYRh60TNtrRdzIA3QAjUuywTkvdJe6mHc1tQe1lKbjKlkKbOsoBILhdoYn667desGeyJScfbsabo9\n9z5pJ2Eo1YDxeOxtKoIFuKRSilQqEadFGfTYmoK4UY2NQl8FNEApv68ThvRk/+gVcyJBGWxvbbWw\nzyBEScqztmBoIK6hF+TQgSKRymUYRm0lDE0tlII0itlcdxn6U6srxFItj6KwrUxWhkBpLyxkTCvu\nNegN2JuLErqxdIXa0O0kvooSpD2Gc7dP3rl5jQv3u+r31rmLbRUUtVhgfHf1VKXIZS6Nx2Ovonx0\neIARQbZFxWvr3kj65Z0DcQzNLrypV16994MWrVx8ttoYX7VNkoiz993nntNJyeV3pFFCHDX7tkEJ\nGsgWlq7MhzDtEUkVLopCAhHRU6GmEKROnmdoJdAtVVMUFX2//2qsVG0mQUVP7As6UUwnXZXvalmp\nuvJeEwIPldOETTW7rEkEWXD/uQuMRSCrLEsSWeOmbkWc3ln5fLcq6Dv/+6Ky7nE2hfIKxoHSft8L\ntGZD0Bj9UJOKirCJAw7FIisNE6a5m4O2yL0wk1LWi/AFQeDpCGcvnuGVy67a8ZM/9SkqobGYMm9Q\nnwTWEIoIXCdZ92v8zt5dXpY9/Ed/5PNQlm0cpjSPPeAQKX/wzDN8VCzlBt0Emwv8Ukfkct5n/ZSe\n7HsEsUcrvH3lVc4M3FifHvR58KxDIT3/9mWMnJtVXlPLmW2t9X1naC0l7IJVhFLKz3tbB1R+/I53\nHC0ODdF8L+9OACjVnEfGf25VV8yExlFUFYnsn+u9AR1RmZ1du8zukiipnz9LLQI2cWF44lEnvvbA\n/fezf8VBVt987hX2d51y8Hj/yFeZqlDTFSRBZ22dH/npPw/AIx//KMOX3kBLLBWp0FfrOutL1LJH\nTLIxas897qYpSqhhiTEsi9K9Uoq5rPmyyDFNRbTWJEI1SlHUgm5IVEa/7/plOe0yn7j1NTjV5+Nf\n+BEA3pqM+Se/8f8CcGot9rY281w5hWDw4oXH1hTYsKFoLMwljT/bQ2XZEAu0QRxxIPP68c99kkns\nnv+N3/4tNi9dAuALf/YLHpWoTegVyJXVVBL/mNISyh574fxFfuEv/BwAvSjiG994FoB+kJJsiwp1\nHKNkTfQ6CWWeYQU10YsSNoRKlleVX8+qLAnlfA3DiFzipDiJSOWs7GjLWM7ZoDYooeJoVRG0x6Cf\nW9q2AqSO6ti4YixIFVnjY2Jj6oWKaNvt7weJ8n1WzcXDFc2C+qC2ykuKJ0HCOVGjepave/5Ooi2b\nwpfQvSUv8T3f36dacxvdYK2DlcCrKkoPjQhDvJWLDTSqsSCtCkKBBkS2JBRI53g0pe/WGbOipCbB\nyiEQ91JiwfJrW1DPD+U7KScLCVQGPwmSOCUTmGmVVeSizBmpiKWOe/7QVh7C2NWKqIFGhdqrEZbZ\njEIsEagrgibAteaey0xjTdEE7h9EU6p9f2Os9/k0xviSvqX20LWizJmI4pepFlR2Te2Df2NqApHw\nsrXxPKODyYwlwUzrMPReT9bWROLPmvYibNVYv0AQNAp8uees5mXFdDz08yPQrSeSChLSjtsMVpZ6\n9ISHUdcVUzlYstmc2chdYmNriQXuVqOoZZ5hTKvOqlsFY6cg/EeD4EVp/z8N+BgWQumDzY0N7x0a\nKsV07ObjwcGRD3DjJPBJhK1T215C/+vPPsNQLF7OnD7Hnqhe11ZxdOTm/uHBmL4Eu/2ljod0dnsx\nWBlgZQn9pqc9XS7PMu+PG4TBPYGI1hrb+DoWpZ9baRKSxE2QHnnbFR22nGZL3aoAlyWz+VS+R8Dy\nkttr9g+PSGTTn04nrG44ztuFixewIpGYZVPmkjDa29tjY8O9tuEuQRM8yW/uHrcqoPKHUZKmNNFk\nXhZMGhhpEBA3wWing5kJd3dt3UPb4rRDEDVeoDV1M2eDgKRRoo5jb6kV6ND/pjiImQssixyCsFFH\nbwOLOIp94sOWFXVRUkmgYzQYuZB005Rex+3pWZbTk4uoUgqrZa4EkecUKVOShk3gZZg13E2t/cFo\nMW3wumDvcY+lRl0znbo5YK3xZw/v9CD18e5xr1nVerbRQv7c31o47rvtH0orHxxGUcyGBPx1J+bZ\n1x0vb3tlwKiQhCA1RpJ0lqC93AaOFwTu8llL0FZMM2aijWBLWkh2WaB14OkhSgc+SRloRSmQUMqC\nUPZV1QnpV24NRLXBmMY/Fowoqgd0mMu+c+P2TawE/qf7XYpmY9DK+zm/5x5e6LsPyt4MrN+7lLXt\nZxqYiOq7TS1zgbUmwYBy4pJ9ocJDow9mOYH4OHbi2Et/aht6zt3nfuiH+O0vO0uJL//e19nelARf\ntEzcJOgCjW4SeRgOBPb+8ksv8OkPfwSAM+sbJEr591VW8fTjjwOQ5TN+57d/B4Af+tTTrEnCsgZy\nK4ri8TJGYqn5LOPyG04tdHp0yIfOiGpuXnpNiLycY5pg1xgPgQXr9wGMpaYpLJhW4TQIvBdlvUDT\ntp4EfkzN4s9tN0eaxIX2hYm6NtjGs7do9R+SOMbKWp4HIWdlPfZRvP61ZwB4Ou6AcAAjY9h700FS\nb+zssiuek+r0Op/9JXfJ7BCyf91dSnd27hKKFsNnfvaLbH/UwUfr0YSj4YhALpZplHrla21DOoko\n4sYhdSlUFFsTSgIsjROsWHoYWxN5RwTQct7ls5pYbLt0WVHKRXQ11UQCbw1CzaHEdt2tDZ7fcXoP\nv/4vnmM0cu+zvrnOSH5nWWsi2fOPhg2F5BhbEw7rtqhgsa02CCEjmft3g5ozn3A81o/9uS/w5ovO\njuX3/ulvQOpipE/8xI95n+kimxN0GlpEhRJXDau0T3LasqDXd332s3/ui2yKvslzL7xEKXtpp7/k\ni2faGqbDA4rI7dedMPBOHirqeK/7pJN473qllS+sWbRXILeUqIEktHRIlcrcTcCKxVNQKGzRxOXf\nuRu9VdF3fso9z36v7QSae9JO2kk7aSftpJ20k3bSTtpJO2kn7fvavq8VUUWrXmW1wkja0AatkpW1\nFcuiTNnf2OBIfD4/ff8FTokP3ms3b7ElQiNrvYSNJZelULakbjLslaEQBSkdJB4CqsMWNmNUe8NP\nogRbC1lca8rGjHhesnpq2fv0LQ9WOCsG67t3dzBSqYkUKPltpamhcO9VFTlzKa/vjSbsixjGffc9\nwulVET65/AY2kgx9VXsFyNDWnnwMKVqyXFoFXqxFKe1T51ppgoZNbJse9/841tZUPFStPDyvqkqv\nqGmtxUh2pq4sed48BwIhjpuAttKlNXUDhTPWQ3JqFTAWyF9RlF6tt9YRlqYC3aoH6ChsCdsGrBVF\nW2PZWlvhoUtOxezZN6/7CnNKQEe6uWssSdkoZFqmZSN+M/PZu+G0ZDRxrx0NJ8wy9xnGtt87oK3o\n3SPuBD6ba8F741oLtfnjVTg/CChvI9K0vtJHiulkec6dXQc9vntw6DNudVWQ5+53V1XOww85+J8K\nDEPxqarq0ptaByokE/jklSs36UhFFK3oyJyJO4lUKd1cDnxFyFJKZWs2K8gmbj11eqmDizTVZoWv\n2KpAEzSVGYWfT+Cgo+Ag2s1cqaqaTMauzGsO911VNw4CVkUN1tQliShODvop50+LMFIUkwn0ujb4\nQd7fP2IqyoFRHBGF7e9pKvVJcvzbbqPap3ToYWPGanJRfa7qOUbQBPm89fzsLy+3FSEdUCwowQZN\nNQDVqg4q5zDq/m49LSBKIl+VzZOQUta1qSsH3QEqY7yQTWUMeVn6taHCwKMd0ijxFVFtod/zZntU\nkrnPTQVNtaSuWF52VZogCNCiaFlZ6wV8jG0FZKzF703GWA9XGx4eMhm5ipXGohuTNdQ9nswNHPSD\ngOa2KFF1T/WuzRnbdr/XLbbM7SPNczQdQTck/T7ffsN5Tn7siScIBcKdj46oGgSPDoibKVBVqKQ5\no6HMBWaflUQCm68CSy6er1HkzqKggYTFnhDDUqfLmogmVXlOlgkE34QogfgrFCxk8b16iA1RQsM4\nqEu2zjuV3c7yEtNGCbm1rX7fTb2zyn2czeJRCaEOfdVWq5BvvPo6AD/6wx9hIOs0NMoLxe3P50RS\nBZ1XGVN/Pga+op8YRVILuiKK+IXPO1Xkm7evsb+3C8Dk4Ii8UUKNtEdDWGuwEmt98skPsSyxUxgZ\nosCgZL8KjWJr2a3Bjz3yAPPcCdtMjo4Yik+sqbVHyNSmbs97W7O27n7Phx5+kNEdEWJUAbsjtzeW\nxmJVg4aweN2vdno7CKWfG8aj1tWCWtA9kOwPgOXSxIfGWIJmZts25rFK+xKSVk4wD2Be1NwVv+g3\ndnZ48CkHu738yuuM5BgsJhmRxJyj8Rgla+iRT3yKpy84KHTYH8DcncXV7T2mG24crl2+zts7rjp6\nMDziVDmX9xyxe+smqaBulpaXqcQTu6NjL1zWjxPCvohoauv3zLosKW2DKCv9Wg4VHsZpVM1MlNn1\nviUVoam4t0Ilvzkrc3blc6fG8sorDj5+/W7G1obbq5NOl1g+4WA65sq+zKvqmNelojGlQAdt/I+p\nG/01FIrblas+PrN3lf/k53/Z/QebcPttpxK9vn2ej372MwAsrS5x55qr8s6VIbax/ywtqCIVxh6J\nYPKaTCqlgVZ88mNO9ToJA559wSFWbJaRNTQ3ZUk7fc6ebsTiUqbWVcyz8djPyzTpIENHaQyhdftF\nbXNvtBzqgI6IFhJ2GIlSrtWtqr8Fv2cFylDLpqWxXvWeWvk4R2HvheMuDtn3cO34Ptu3KA9XKFXL\ntYxD5Y3fZ7Mpb1x35snPvvYm9tAFBo9vb/P4wB1q9frAG9Gbak43aXwGNIVAHWwFmWzidj4nlQMu\nqjTGNtCjhESUP5Wx2EbRt7TcOXCLSMcDuoO1Via/CFjrO3je7Rs3SaVUH4apj0xC5eCo4GxJ9iTY\nvX008hfuU6t9f2iMjobEMmpRHBCHDeeg9JtvGKck8llJ0icQ02sdBO3CQnmOkV24iDafeZytCd6s\naeFvjsfS3EQ1Rd5cRFvol7W28U7HBBqlWihrw8cwC/+rFORyGB0ejrHGXQQ0mgXkHIhEfkDLAQi0\nIozdYVrOC5Z6A5KoUcfV7ApE6e0b13nq0Qvy99rbMuRFTdnIZxc1w8z9/e54yq5YcIyHY3+BtLb2\ni1Zjva1OvXD4F9Z6HikoH5B1koh5k7yoqnv66wOD71pIJejs9yNq49bL4WTKjV3XN5Oy9vGttSVG\n+JxVnZOGbj5+6OH7GIo65tvXbnmeUS9JiCTYfevtKywtOfhz0omIBDab1NpfQLRq54kxUMq4z7PS\nJ5W63YQgsN5QvDIllQTLlWqDcXdx0P69atvCL5vLvDGGUtYmteGzn/lhAO67cIlbtxxH54lHz3sr\nEWNqtJxck+nE7y9KBV62fzyesrvr1nV/0Cds4Oa2RskcbaC+x9msnEbj0Yw9uVB3dMD6wO0Ta+vL\nNEJ4+e4uuVyWVRx7mL+7MC7A32wDP6soG+icqtHN+lKKUMau003ppO6ziixnNHaJidlsxmzm+nie\nZdgmWK3re+Z2Era8oziOfdC9W5Xe7iBNUw/XNLUz4QYoFxJ2SoVtckc3bHEw1Gir/Gsb+KNWgQ9q\nh4eHjIcuGIoDTSiZmUUILCwgc4/b9oN2r1688GMXL8LGf19HX5bn6MAnQpUO2Np0PORHHnqcZ2Uu\nX9894PSZhp+syU1zYYdQ+iM1eON6YwwNR6LX7Xg44q07d0glOZAGEYPlAalw4Hp1l3jsztTD2YyB\nwDhXNzbQM+FgF7kPDKMk8Dz8Mq98gqS2hkQSDWWa0r90v3SQJvTfDxajnkVbm3d7/J32T/0+1B3f\nU1P4C12gQm9BZ4KUF687Nf7n3rjGF5/6OADdSDOSLaEThnQEvjvbP8KKQnVVF/6ssHVJ0cQw9ZQQ\nd25c3Nzgwra7qNRAIBe9qirJJHFgTe3hwSpQLC9LTFXV5NmENHEXyE4cUtUuMA/rjCX5fhc3t9kW\ntXmlIkrh9WV57qG2tbY+oTWdTr2S7GFR8MJbV1xfmBDbzD9lCHzW1ra8Ndr1UDtmJuC0ANRCMtfY\nxYj4eNuiUq5ukmi1Ic/kBhOG3vEhjBQTmZt3phlT6Y+X33yDn/zsDwFwd2ePJ3/6xwHoDwYUt9x+\ns9TtgagUxxsbPrbZf+MyN1/4AwBmN+4yuuku9dd3dkkuOrvABx69n/nbrwFw69XLjG/f8LFUFIX+\n/J5Wcz8nEm0JheaQ9lPSyK1ZUximohhvqzmGVi28GchaG6x2O6s2JRuSRImShFoSz+N8zoHQzaZV\nzd094cLGAd1E3mc+IxcYsNYptViHJd0QZE4fS1NeSJ1At+m6IAz9HcTamrncJb/89gvcFajtxuom\nZx93l8YPn9ug96AkZHZ2sELbmsRj0tr1pbYKK0q3KooJ5QIYRDG6FGhtVVJOXH+cX+8y23b9d/78\nJf8939q5yd1bO0wbx4JqQtpz/3FZxf4ss+WUQIoJJsQnvWpVgpIEQd2no8RNpFdRh258YyIP96+U\n9XY+Wtee86qxqMb9oQArNktUeAs9VStsEx8Yi+/h98GbeM87sFIqUEp9Syn16/Lv+5RSf6CUelMp\n9b8rJYSTk/YvdTsZxx+MdjKOPxjtZBx/MNrJOP5gtJNx/MFoJ+P4g9FOxvFfjfZ+KqJ/E3gFWJJ/\n/5fAf22t/VWl1D8A/hrw3/1xb2Ct9cp7RmviBqJpNch8CqOQo4NGNCSkkurB7711lUfEF/AjDz3I\nmQtCgDcle+I7pAPdZscCUFKNKOoCK1WTOlSEkmVLo8j7UdVVxVygmqPJjEgqaVlpMNYShI1hLR7m\nW5YlI4FybZ9abwVUlKKSCuwsh6N9yQSPJmwLhGx7eZnf+6ZTrLs5LRmIQMRSrAjlN0RBSCBwxqTX\nIxZvrqDbIZDqgda6hRovwFTeVVvDtT/xOLrfLukQ25rGB4ETtHHfS9HIV1YFHnqjFJ4gbsu6FS/B\ntmqfC9VcbSoKUW199eoOn/vYkwD0qL0PmwoDX50oy9KTyLO8ZCCV4+E8Y+doyJvXnVH00XhKmLgs\n4DdfeoMnxOvs9EcewYq4lC0y70s5nGbsiSDP7Z1D7oi316woCaQiakzVZiKVIRAIpgGqpjpXll6p\n1YkhiJJsknhfpneaejftHX/7k4+jsnQk457lhkqqVfv7Q/bEZFmh0EELd21VPQNfpQkC7RXXp5TK\nzQAAIABJREFUgiDwML28KFBifjeZzb1IxsZ4mbQjsLQoIGxeq0Hr9jfORABo/2CfWnzO1qJVkk4C\n4pWpZnOvFD3PZl54Jk1TL5pSV5WDy0sfeuXusvRwwW63y5OPPATAU089zO3bDvY0HI48/Cyf5+RS\nQVVhTVpJJtIaL55WFjP29w8A2D69TdgIv3znUfgTj6NCEco8tzrk6k1XATNlztrjzsfszIVLGKkY\nTMuSqXimks19HWER/mcX4Gd1bXwfa93usZFqfSx1EPjKRw1epGEynZKJN6FWyle/UIper0foodTK\nz5tOr+8Vhg8ODymkCrqUpCjBkJq8JhJxBUPox7FjaqqqKbcFrbhCW1BBK+Uh3LYuySSzfXBw4N9H\n6cALXy2OnrXW//5jX4/cK6bTIlpUW/dbFL9ZqMg6T9FWQG5l1Zmtf/HPf4lXn38OgBffeB0bN4iV\ntpKklPIolSow/hwMo5hO6lAMURR7BWylNFHQwOw1RBGhIIuC2Rwtaz6fZxzIPrnS7dMTfz0U5IWI\n15WlnzdFXVKYBt0AHUFcVHNLP+nL92775bjaBzGOXmVdK38+1soyEjGs5154mc897oSC8towbegk\ntCJnRZZ5dE5ZaGwjMkKFbaC/KqLpD2MqH8/EcUwhc3kyHnrhwNXVZbQgM0bjMSOByu7vH5DnBevr\n7jVpknIg451P5myeclQoWxeYylV8wqCm33EVnwjr501R19AI3hQ5pYih7RwOuXr7hvRF4IXyuqFG\nS3lFLajmBk7iVH6b8WtZa1pVT2OpG5j9vXWVYxhH68UNtVYekl+V1lOgFJqGATKuK3bkzJpaC+Ip\nvnN4wMtXXYz60Cc+6oV+hjd3PF0gDAMiobRUl69w44pDBb7x6hvMpUKpsorRgXs8mky4cMGhuJ77\nzS8z33HPv/b6FQbpMlbiz3wyRxcSY+iAUpBFKu34ymcahkSibl1q41FqQdAiLpS1rTo9LRVk0Ot7\nGoU1NZ1lhx66+sYOd8YuNj6whlpi8X4/IGmoBUYzkHV9cGePjsRa72AoHcP5aIkk5tfBQkVUWVTU\nxJ8KAWlxMD7iH/1f/xiAv//v/Efc//Gn3ft0NEVzvUhWKMSn02Y1VTmRPtNeldrkOZ0mNq5qj9qz\nRUYlnt6T4ZAHH3K0g3qe8+Vf/00AvvbS80ymI7oijDq2GTeP3NhZG3Fuy+3v3TQl1iL8FgXkHiZu\nSLQIR0UxpXi4F0shDfK5Vi3U1liPgse4Oqh7XNeNWQmmtNhSzr7Cei9ZtfBih3hq72Dvtb2ni6hS\n6hzwReA/A/6WcifgTwB/RZ7yPwN/j+8yIYx1AS9ArC2F8CKTQLEncJE4SrzZ+urSEldEgfMwTvjH\nv+/Uxoof+RjTJbehz4e7LEfuPZfiDghkoKwLOk0QqLUP/m1Z+5JxFUWeXzabzunKAZD2Bszn7vn9\nNOLKtRvYwOHEK+BAJP47accrYGZZ5i+QKOX5bUfDkccFzLKc06IgN57PORD4WndlBZuL7Lpu4ShB\noEglEOguL5OKoW3Y6xJ4Rc72IgjK87EWYUtNAHpc42gtXh3X1DUNGTKONf2BSFoHisZBJs8MC9Qz\nvxGkUYyVA7LEYBolPGX9JTaytZ+kf/Di2/z8T7rfel839zAGrPGww9LUBCL7v79/l2tXrgKwc3DA\nH377ZV5+7bJ7YrKMlmAq7fS4fceNb1bcj5VL9iwrGMoleDTN2Bc1t93DEUPhQhQ11E1gCv4Q1brl\n0VqlMGULNb6neT6M9Y/faSfwTmjZcY0jKEKBdVgU08YO4+4BY1FxdId8w4nTHjpnDfeoFDYXlaqq\n2suagkpO6bIyjMTCZzieeuuTKA5JG2W+QPmLaBBEDOXi+uLLL7O05AKh9dPbFJVq7VVyyy2BuxV5\nxkMPu4tXWZb+wol1QS4IHFf+PpvNyOXw397eIpb9qKTk1IZL+vS7EfO5qIXaPrHwt8Ik9omTsqo8\nTPf2zZscHriL6Gg0oivKzkGg/8jYH9t6BKrmUAhTDsaSUBsdsrLq9oxz50+TpM3lIaSTNvvW3PM5\nFdqPqTG1vySCFmgcVNZ42J0OQ59UyqvCy9aPJ2N29xyELM9zliSB1uv1mc1d30+nUyZZAbbh8Qde\ntblrNXNRW53lBZWoPvYHAyJRC9TathZVQTv/rLG+n2trPSQe2vUThBolv7MocyaTRiH6wM9drW3L\ntTXmXeGbx78eFy9F9p59oL18tpYtSmkPF4yiVhla6YBIEhOPPfFRzgjf7NXnv86uBIeffvppr3wb\nhAE2dJ87qeZkQkdYCiN/KfL8TqCTphxJwF3ZmqPplN5tl/yoisLDxE2p5KIE+awgbKbfgqpsVbcK\nxrW1NKzhHIOS/fnTH/8USd/N48y0wZO22q/Bd+bt3s2a5Tvtq8bDuo9pHC1+LShUq5iuFZXMx1du\n3eKGjMVqPyaRxHfPKgpRx5zP5pSSxOkEIbXsW0VVoQViqSLrbXjQrQ1elVUUwuOzpmBl2cXxdZkx\nGbn9qSxLckm6dnsxnW5CIfYvk8mRpz+sLsUo4TIGYUQtMF8XaApHXCu/R1hbMRYtgbLK/H6xNzxi\nIpQl2+myLHtQN47aTLWxPs0QBoFX0y3yzH+W1h6QjrG2texTDY/9uNajdRwvnA1PE1nX2IZaSBRF\nZBJD7s5yZjL3dRz5OWlqxVe/+W0AvvA3/hrjwsWPN95+i+2z7uIWm4TxDXeO3Xz9Cm+94rjExbwg\n6bh9cT4vyGWuLq2u8tYrjvv9+7/7ZZi6udTr9lgZrFHKuTuZZj7oCKLQQ7FNAN3ExZazydRDyauq\n8rSysi7uGVMl8zjUQbt3KO2dAu7cvM3SstNxuXzjNvsSI+0WJYXAyquyZirq9EejmOUNd6Fa6Qyw\nxn1uoULg6NjGUQFhc7lWuuX2K+sTOoFSnidPB379204l+tEvP8S/+Rfcx02HU7QkPzudZWyjfl9M\nPa2pNJYQOaOCEGUbZ5CaQqhXdZF5hf5pXXL/A25/vvLmTb7+hkso3MwscXeNSS4cYlszrIXzXeTe\n7imMQlSjiqza5GkSKFLt9t66ttTL7jvtxod+Dimr/flYGeu0VnDaKqXcVsvCUsudzeaqkWWAEk/b\noLaea0vdJmzeD1T+vUJz/xvgb9PSwdeBI9v4F8AN4Oy7vVAp9deVUt9QSn1jPjtG3PdJ+17asYzj\n8TMxTtr7bMcyjo3Nykn7U2vHMo77+3sf/Dc9aX9cO5ZxPDw8WY9/yu1YxrE+ZkG5k/a+27GM42Q2\n/eC/6Un749rxxKvluz3jpP3L1L5rRVQp9bPAXWvts0qpz73fD7DW/kPgHwJsb29ZvCpmgJFMXllZ\nJpJhff3t5znacxm79aUew77LFKbdHsOOy4r+5//0nxOX/wyAz37oPv7y5z/p3scoEoGyWmV85iOM\nQ1+ZsVWFkplZZjNMIBXaNCGQrE1/aQktWY28rumvRWRy+x9OZ3Qlw3dqc41QyvDz2YyuZJVqFVCJ\nYl1tctKBy0beOjjgzTtOAW3n7gE5IiBQ5mjVim0UUuaOk4Rur62Ixn33WMcJNMIFBl+R1Fp7OKLL\njDTpYgOwzDGNo1LKLiqSNtiqJIm8eElRZBSZ+47TaUlVNhlq6Iv/VTdNmZdu3IMgQAcLxPgGsmKd\n+BPAa1d3eOEtB+259NiKr/xb1X6HINQeOn3h/BmKkTtMgshyenuNJyTTs3dYEgh0Zm3QoStqd2Vt\nqCSrOcwtE4GLTGY5Q8lCjaYzbxJfGUUly8go7TPe86qFPGqtfUWhrBeEiBYS9HVdtwJQ1v6R7P1C\nO7Zx/OiTj9lQ1osKQoYC+7mzs08pFVxr8RX3whjyzGXf8qzwv6k2OQf7bl4f7B8wkwPcwalkPlQV\nw6F7//3DQ/oiohPGgRd7SmLj/WmTRPvs3utvvIWgJzkcjrlz536s9NWVt65yd8dlkj//kz9KV6DG\neZ57RWZjDGXdVmyb7z2ZTIjFE3NlZdX71Clrvdrt8tKAgSh/aq08BFkFrcprba1HHayvLfHWG1cA\nVxFd33AZ7yjqLCiDKzjGcXzywx+1M4FcdXoDYvHEffWl18kzt752b9/g9GkntGBNQVE08Evl1bjL\nsn6HKE9DNQh9ZUwH2vv8labyGdKiKihkboyGIwqB4y2tLDEQkbkgjOkKnAkdMJvPPfx6NhqzK0rN\nVJc9dHOezxgM3O+ZZ7lXwa2qmiaxmdNWuVEtKqGGBdpCK8Sk1IL3bF0zFmTKcDj0yBlt8Eqj76yG\n+h5SxzuOjz/xhF34u6/sugpQU+1Ufo+3Vvn1orXyfoZGBSgRIlle2+ZHf+LPALC3e8V7/E7mOWql\nVQIuJbYbTY9Y6jj0QV1VXnjLIQkaoagWmph0+hAFvsK53OlRipBLQYkSzG+R5eRSaa0o/Toyxniv\nUldpco+zuqJ3yokCbjz8EPMGHlZbL9ShrG3QYfe0d+6d71Hk7djGMYki23xmWZUe4q7DgFrQOi/e\nvM5XXnwegJ/+7A9RSOWzZwOsrOU4DJkJiqQXJVSNGnkUgZyVVZ576kAUxAQNZFU5lXOAOAo42HeI\nn9u3bzI8cnMgq3Lm4gHe6/bY2tpmbdX1eZIqbl13aKLbw31KoUKsrmxizip5vE04kLVmKoJGlclW\nXhE3r3O/YK5cv8ZEqqnLy8v0um5dL3W6mLClNQVN4FYbj9aI0hyt2jhnsZpdNF6Nbs85tnG8ePq8\nRarCSuHnfBQHHvE2LHN2BTU1rRSEbt3FgUY1gothxLWbjupx+dYOD37EoXae/epX6K/K+WsNz33j\nWwC89cIbnsKQpD1s5fan3NTEXVcdtbpFdZxa2aYYuL09z+bszwvvP79xas0jW2rd7t0ZhqShmM3n\nFIcu5o6TxFeYG9AquH2ngebGYUSxkGxpPLRNWTNuYojbdzEi4jSdFhhBJ3a7XayI6BRYH1c/+MBD\n7Mj+X5YG3rx5bOMYLWmbym9VgcI2bh26FaGLjSaRGDxLDOPQfcdf+c3/kSR2f/9LP/El0sDNWWKN\n6krcV1XE8v61bmH5SRwRN5Q+pWhUy0wcsS/xy9mHP4RddgJyZ5/a5m/9/f8YgOuHe1x5423+xW/9\nLgD9QBEOHEIsm0wJ5GyqdCtcpEwr+hUrqGW/nU1KcncEs6umXnwoUBbjz8cWmlsb6xGM1qoWcVHj\noblUiy+wrXCogQaE9H5sfd8LNPczwJ9XSv0MkOKw2v8tsKKUCiU7cQ64+d3eSKn24IzCiMa82wYR\nq6ecAtjDWvG13a8BLmjvyOVrOs+Y7e4A0O112T7t+KLdlRXmU4F7BVO6a67UbwkoBY4XqojANuqs\noZstAAucNGMqJtL7RkEtKoBJFEMQUAtka3OlT1/gbtPJlJnIJY+zOcsCMu/EEUY4D1GSMJEJ8fbV\n61y77Q6E/tIKPZngS5GmK18p0aW3muimIV25tPW7fTqCp4/CpI2GtHKy97jJ5A/hhUkgl4E+xzaO\nrQqZNS44Beh2ewQCB8jnFfOZSP/nNU3xXWtNIhfAQAd+LlcotGDatS2IGqsajJdXnhUZf/ic49V+\n9qEfZ71RBdQVVQPwjwOqBtYbJAzkoDxrLD/z41vcEXjt5ds7jOV2kyjNuTWnXDbLCwrhng6zKYdT\nt7EeTCfsCeRxXrbQ5MC2cNICkFiNWVlTCK8RZf2FzLJgG2AtujEUDpS/TGulWgxvi0Rr2rGNI1hk\nOVLZml0JUEazUQs+Mi20rNaKiRy6OwdDb0OTZzOu3BCj7YND5nKRL4wmlveJgsDbuuzvHrAi6zpU\nmkAOI90xaFGZqwPNulgAfPTpj/Gbv/0VAL721ed57puv02BBnnzyUX7uS18C4NTacsu/Vcqv/2w+\nRyjiVAaymSgh54a1dbemOp3Qw1stEao5rKz1qohWWS9rjjWouoFMQzO/kyTl3EVHLbh27WrLwwva\ni3Xonnts42iswUhgv7p+ivsfeBSAt157hes33MtHe7c4c9odeFtb6z4R0O2mhHIZVyr0vK0oDFFB\nQ22AQHhNYRDhFa1NQEM4KcqCI6EsjMYjOsIbCsKYuSRzqirzXFqlFFES0hV+S20rJrI2x6MxE4Et\nBoGiKxC+8VGrVGyCgNHEBU/jQjOZuefXpsbqhrOtPG9SW+XVgZWpyOV98mLuPyub5a2KoG253Eot\n0h8Wmvvb8a3Hxf37O/AgzcKGEAaR5wZjFVHz+0KNbmgvvQF/+Zf/LQDKYso/+p/+e8CpPgeBFBNU\n6GFZhdFUgVuDs3nhL/tFnruzE9A68FY9SRjT7XZbTqQFU7hxrKuMA1HTzurUJ0wrW7HUd2NalJnn\nm5lAk8v7dFZWefJjTmm0MspDNxUt3HqB9vtHQGDvdvlUC5Yt99h+uHaM+yq+P2tbe95mqjSh/L5Z\nZfgf/vH/CcDa5hYfeeCie91sxLef+6b7jkmMOD6Q1ZZYLvVJvNCXde3VrW2gvOUSWmFCgYzu73L3\nroNOYypsIBeB+Ygwas7KlL2dOz7BF0cx89Kt5yqces733cNbZJIkOnt6xCnlvnc37aEkqVmUGbnf\nh+FAuKOvX7lKU4iwi+dK3VJroqSxaoLpbO7Ht7u0QtzA8pW6h6fdJKEKlwA9tnG0KIxc1JUtPPyw\nrCoy+cfOrGAuh34QhujmQh20HNjKGEqZv7evXGb7jCtMrG+vsSy0hf3bd1t0Yxjg82qVQRgCRFHC\naOr6UoUpVmgiUazpeLj+CsaUVGVj3ROgG5sgFXr9jKqoGJmZvG+Ilr/XdUElZ5yNFi7fYYQRuprO\nx0RN8thaerI/98+e49Z1F9/qSUlX4rMNpRgH7vnnLmzRkVjN5CVjgbSefuA869sO1msKBX/w7WMb\nR60hkf3QKrsA229hujqs/R7WU9rDTgum/JPf/DUAZnf3+NSHPwHA9GhKN2340QGBcDk9XQwIYo2R\nhJFVkAZurEdZTm/DncW2m3DzroPjYmpWltx32Fw7ywODHvdJ/L+7f8hv/ovfB+BOnpEJzHe3zlha\ndjHMIO611JrSMhHN+L3VnKuFSyhaCl9eNk6wxf3dKg+DrwtDnUuCKQdZAuhK+YuoqRSm8Y0xxl8+\nlcXTdUz93sG53xWaa639u9bac9baS8C/BvyOtfavAv8c+JI87ZeBf/oeP/Ok/em0myfj+APRTsbx\nB6OdjOMPRjsZxx+MdjKOPxjtZBx/MNrJOP4r1P4kPqJ/B/hVpdR/CnwL+JX38iLvuRQEHmLEQgVo\nqTfgwQed+a8l5ECglRQlkaQbessDHrvfKYb1x0dehavSEblk2TpB6OFX1tRe3dGWNZXAkHStqaqm\nTB97s/QwTOiKaiMoVBBSyC1fmxCkglDEMMncZ8yLOWos2eMgQ4sHXZCEHO4IHGo2pSeVoJWVHqFU\nzAJbIsgFIuUU1ACiQBMJrCLUkYfQqXeanatG6dJ60Y730b6ncWxyHRa8empRVFSilldVFWXZZjyb\napBSeDN0bOmV8BZzJxrVqrWhvEBOpeAlMWe/M/wU3TXXx2VeoWQqhzZG2wZyUpFLRn8+zpkXFXOB\nh0Zz0JL1meYlV0eu2p6tZ3S6olpY5xwIZOVwOPv/2XuvYG2v677vt/dT33L6+Xr/0AsLABIiIVAW\nJVOiY0mJHU1kK5pMxpMijy48unIu0q8yuUwyyUzG6XEylq1iy7JEU6IKSVCsAEGAKMRXgK+d3t72\n1L1zsdezn+eAAESCh5hcnDWDwcHB+77neXdZe+21/uv/ZyJVcWPxurLW1E1lxJHiCElFUded74av\nHlprD6WJmv2gtWqrCp2qqdL6+5k43tnexzy2z5LlBVsCr83ynPZJWg1Dq2Bn31WPiqpiY3PLv74h\n8JqVlRfEdtUbNzaXL10gFWKkne11doSVt5/26DcM0LbyMLMsN4ShywZ+4ic+ycKygwvevn0PRcCl\ny66a86GPPEwiWVtbWKzATqZZ5rP4lTWUTSN+3SIIgiBiYdF9bhgG1A3JijGeFbqua78HjbKeNNja\nFnIHilgIRpR2mpoAly5f8n5EqRZOpt47//f+9qN89sLiCg8/+jgAr738Am9ed2LZtcVXhXXY80Q8\nZWk8m58OFU21M9ah13bWgUI31bAg8gRjWrcZVWXxmrFxEHp91gO7T5o0rH5Jgyj01am6ybJXiqEQ\ns/VWU/9Z0+mIsWieRlHAklTSjLXsiWD6qNRMpFIAylclVGloWgjco8qzYjwRS5Hn5IKMyLOMsmha\nKgrCqGGPDNpKmmn3onrvfO8PP4+q9aVdHVHlftG+qGU18/qpgQ4Jmwp2oD3ElQCGC26NP/7hpxgM\nXFZ+PJ74PVF3iVisZtqwkStFJNqwdVWTSkUqCSJ6sid6aY9YRx6+XFalh7tH80MmQoZzMB17qFaY\nhEwEHl/kOalUzyt8gZ0LV65y5dJV/7eVbc+J7z8x3p/9gJDd97Ufmwp6ZWtCDx9qiXgCHXF3151F\n/9V/9z/yyccfASCtZuzvuLPo409/jCXZX1G/56HiUR7SE9ZxFWrP6K+0drqRwGQ6Zn1bGGrrigUh\nOrS1YTxyrw+DlFT29aC/RKB79GK3B4si5/TQodQOZjGjyu1BHQaedGp/usHohvsOJ0+c5cSKg4ca\nhWeSrY3mtRsO4ntnY5tAGNjLvGQ0cp9ZZrlHPWVJcohMbkFIluaiBc+u3KAZAMIwJBTfG/TfY0Le\nzzxaRd2Q3JUZNU28ULAnPmNSWB/bBLqzTWkbG61Sngr2G3/653z0YYfme+DMRSZ7+/KqwBMMGaWI\nmmpbEKGl2jYrKk+MWNUGG7TajYk49zSNiYPYoxcstW/jqmvr45MgUB5ZoKPEo/CCOELJGipNTdVU\n2zE+zjRl7ePVhaTnJ7vIS7aFpM5MM89UuxhaYtEOjaczTiy6amCZ1r514tyl81RT8cOjdn7fwX7o\neVTKEWlCg4Rofq88ssCG7bkcGhhKZd6WFetjN0e/95U/4YsvftN9j0LzCz/zWQCunL3s28cCa9Cy\nH4uyIBd4/GAwoNJCsFruewKvN158g0jO4kEQsf6Wq1zeffMttte3GY/duba+tcW+6IPbJKUniM0y\nNExzOR8TxTASDG4NuXZn2e3ZJvvyt1VVe1Ivq6zXvbdoanG+dWkbji5s6aqrAKq02Oa+VHVI2GwL\nzT2EUvkhsLk/1EXUWvtnwJ/Jz9eBp3+Y98v7/L+bn03nAvW1r32db73gGMZ29/cpCoFHaU1/6A7U\nqjZ86c9dmfojJ5YIP+Q2tok0E8HWR8b6oLYqOhchrfxRZkyOUn3/mlouMHUNU3GGeVFglSYQZ1fM\nKsZymcmmU7Kq6f0rWOg1FPOGZmZqZbjZSEFkuRf43t3dZlFGv5dGvgeoNra91+gI/LO2ou22wyz3\nbkz273XIHsk8eoSi8j/XVe3nsShK8qy5iLaQMx0EGHHKtjbMD92YlaVlOnFjWRWlF1UPlO1gzjW3\n19xGvbmxyamlk4BIokjCohjvkI2kP2Wcg/Tqrq9tsbs/RQmUbWc6ZV162nbznGLqgtrF+ZBnf/JJ\nAObneygJ0PZ2Dzzo3UGTG5ifwQi+uDKGXHppatsNWN8dKtYwf1ZV3dkbxvdQYXnXi+iPPo/K75Hp\nNPM9nF0HEnSSRNYYxp7NdMamXAQs7fcoytLPda/X48JJB5U/ffIEiQRSo70d79y0DWilwCK2Nt1n\nvva9a4wn0h8UJRRVw66qOXfuJPu77kL82//kn/s+jCSOfSCW9iIP7e31ej5YaaBJAEmSsLTkejjH\n4zFFId+hKH1/qdaanlwmwyiikn1dlgWlHDJJHBEnDcQ6oh+0AXsDW+/qh9i3kZn8qPOoOvOYpD0e\neMhBcz/xqWfJpRd0tLlFWTdyQqEfc1OVPqBRqpVQqqraMyYSaPcPgFa+N8kqx5zrvqv2Mg37e3tM\nhTFxNsnQ2s2VJqSnGyh06p5c2I+TOiGTZ51M9pnI4V/WJcMFN48nz55kQXrYbt6+w4Ec0hkxU4F9\nW6U7N5ROv6tSLYulsUIzD7Y0LdNomVEIe7lSVdsPY4O2R7Mz7lod3pc/+jx2EPmqTQChVCuro+yh\nntXGZ4Rh2MLMgpZ+X1eGSCRbLly+wMqK24+TvR2m4qtza+l51kzjgk4cI2hzbo7zjPUN53ujIHBM\npzgIZxAGrX9XGtPAw5T2/d8KQ39ebgqhopBkxsy0rOhWB15CamFh0f/t6vB2OWQ/4GXyHe3tbOSd\nz/wzfsTzsWHiNRjPHm86jMdWaaxc2O+Np/zeV74KwDDWPHDRXQD3ASU93gv5GL0nSd4yJ0pW5Dvg\n13sU9Tyr9Gh9zcMz4yQlVO73ZW5YkJ73YprTl/6voEyJkwFmLL6gDkE4HuwoQhWSLBwE/vtkdUWR\nuYtHPNKsrDhfmpcFjbrb9u6Ub7/2BuBUAxqYaF1UjMYSR4Wt9FAYtlJMQdCuqzzLmI6dTynLwves\nB0FAKsmSXtywfDs7iv3YJIdtEPmWn3Gdsy9xaVcyRhmLbCMCCy09q6Evwf/LX/ka//lLLwOwvLLM\nJz/j4OdXH7qfdYltDIqBcKMEQUium2JHxo60Co1nu2SyMUJClqTFod/vyf5tOB4Mpul9LHOvqpCm\nkU8s9esey/L3KoVPbERWo2WyDfg+4bosGTdqFmmfU9LLvb21zfXXXwNAG0PUwK2NYq6Slo+DkoUz\nomowy4gXXOLjkUcfJZDv88ar1w7Nw488j0oRh02SmdbJtvcnsNr3VytlfRyXRBELQ3f+7N0+4GDf\nrc2TgyV2JF66eMJQiUSOCQxRowKglO9/L8qSWpjJszzj5RfcGvjO116g2HEXyXtvrbE7dn+3HyZo\nYE/kIXNTMZExnOmAZNGt+bnTQ4aLPfl7Jco0feGJh68XxlA3Z4bxHWDOR3Vi2oZHp67a85HuWVlZ\nzwlhK9vGpZZD/aD23S4l72E/KGvusR3bsR3bsR3bsR3bsR3bsR3bsR3bkdiPAs19X9ZJkq6qAAAg\nAElEQVRUTuq6ZWgMgoBQsjOXL13lrVuusX57Z4/ZrGm6jslHLkteoNlwSQSK+68wDV12MC53PSPu\nJMtJEiH66cVkkuFQoaYnlcuqNOzvS4VnUjE6cNmOvfGUpQuXARx0Tyny5rmD9uc333rL0boCK6vz\nHo4zGAzYnThI52Ra8vpNVxHdmdTMhAFNl1PSnvv+i+mCF64mCD2Jk7EtRLOu61Yvi8PVtqb6pjuZ\n8679KFnjdzJr8VlRByMVcpqi8pVCUysPXVSdtK3FeKbhxYV55uZclXs6LagljZpNreeTMkh2EUAF\nHMh6eOH1N3jqEUcKo7VBiQbV/CAhlJ97aZ++ZI7Pnj/PbFqzsesWzuJ4Rrjn5qLa2SKT9N3Jc0v0\n5ZmW5vsszAvxRn3Hw/xQyhNEoIzXoiqqsmXw+/5qyTuOZZM5r6rS7w3VySjZQ7N99FYJ0cLOzq6v\nSLuv1laAmr9fY0HgmqU1KMFvmLpmKFXDRx+6ytWLTqDZVBUT0Qdeu3eHc2dcpn9hOE8lBF7ZJKfo\nu/mKIs2ykEZ99KNDnpes4euv3SQOXeY0ihVl9pZnsu1Fi2xuuf3VG+Q8/LCD9V+4eN4TTeR5Ti4Z\n7LIsPRPj4olVn1m31J6ARevAwwuLovA6k2mvRyTsykmSEMUNQVHooT8a5XUUldYevuLmv80gHrU1\ne7Aylr6MzRNPP82GjM3Xv/gcOm4YxVtILbbyFc6oQ8pT1ZWHD1UdNtMw0C1MUrVrU2tNKlXn1aVl\nyqFApKcz9gXOXVYVdZuOZTAY+HaLvKypBBZbzKZkQnBCAH2B4y4sL/kKxPbuPrvyub2lUyjdVHvb\n6ry1yhPF6A5RDcZgG33bIqeWylGejTGi1RZHTusaIAraVoi3Dfo7TcWRmKuItu0J/qwM25YWpUIP\nbVNK+b0ZRFFDiEUaBSTi2y5cvswjjznY9re+8mXWRad3ZWmOtIGfG3x7RRIFng1yZWGeUsZ7tLPL\nvsxjvz8gSeI2OW4tk3FDVlTTqMQPlufpS+Y+6fWYNERikzGDsGGAN5RVEx+0zOHvwSDu/edfwTTu\nX9O1v+r1P4rVDfEHhmbDVygigaCGCs8wX4YhTSg2s5aRtPssLK4QFXJGZRMORHswL0sS0WEeDvrk\nonPskDpuvg4mY/p9h746ceK0IxbDkYw0PwdoQql2hEGCMZpMKrBVndEvXPwUJj1igf+RwNJpYQLv\nRaytO66Y+YVVckGjjcYZuaAJbt9Z500haCxq5ZhjACpDqQXFQNCEURRF4aG5g8HAV69mk6lHohlj\nyAWhEWjt2Tt70dGGs10iJGMtk7xBUBVkzRmtW91EpbWvoGqUl1k0dU2kmxYGy9ot55PfuLVGLhXU\nU2fPEwr50GAuZOWkQ3s5rWs3v5WpSQSyG8YDkqaVIer5ynacRI6JWvaX1njStaKqfNyitfW+3lQ1\nhSAX+r2+Zy0ui8K3vUxnMybChl9MZqzfdAQ7OzffYvWEi7HubmwwPhCYqG7jNl0baqG9DoMZBzcc\ny71aSPnkZ34WcNXhSuKP02dP/5Vz88OYUhCJD3S66O2Z7MM0pdpSYYcMK68rL/Rx9txJ7t1xsPnt\n/W0mY+c/i9EekeglE1pmUtG3Go/sm+UZcej245s3Nvn6N14F4NTSJZ78xEcA6PfnSSRG6gchG7du\nce+O218337rJd19xbTa3791hJPrbpqz92V8bPATcqoCRoHtmpqLBgWmrMVXbgtSNNZsriKkVxkNw\nafslaloGXeMvAIfDGaVAt377Bz0hP/CLaAMNqKqqhal0YLonT53i3IXzAORFyf6Bcz5bO3sYEWgu\najj/2KcAeOqXfo25eTfx8caXPXZqe39ELMHJ/GDgL0VlWZP0BKZSFwwGrgdhaSmhKpoAJiAvpWdh\nMsVY5dnDLBaEgv3qfZcohTVXqYpAyv9JP2a67Wb11p1t3njLOZ7aJp7afj5JmOs3TqtzsVSaUFhl\ndRT739fGHDp0PYzzbeN71JfOd7PmzwRaeyZkay1V47jb8/eQmdr47zs3GDAv8jTKKHZ9kFp7+rG6\ntgRN4d4qL9r+lW++xC/91EcBOLM4pL8oguBpTLroxi3LS7QcutNxxngyJRPJnEk1opCkQC/VJEKL\nHiQhm9suiRDammzmt7C/2JiaVorD1N7ZVB0pgkNj1b3NHRpD6+c0CEK0MMtVHboxrbXvLzX2h6Ah\n+wHMWhhPnLPa2Nj2Fw86zJxKaS9NogLlJDsAHWlOnXCXxscevJ+PPPYYAA/dd9UHBG9eu8bv/v4f\nArC+seUPOGXwbJxah4TSP5L2Away9tPBHB/7mJvfhfkFD1sazscsLAxIpIejyA2JJHQuXbnIqVMC\n17a1P0yqesZMenqmkwwjl+/hYEAuvYzDQYyW/iWjjT+ky6rykFM7Ay2X1TgOiWR/uliowayAsrJv\njf0+GC4chrEchVlrPSOxDZSH5y2fOMmpsy5ZYzDYhj6ewvd/xqEwU+MCpqrTV28l8Ih7Pd+vG0WJ\n70W0pnZrFQfX7MnfVdb6ZNrC/ALzQ3cA51lBg4yO45g4iShyuezpgKEkG5Qu/XgWtiSRy39e5R4e\neufeGlOZ06WzA/oD2b9B6LdIqAPqRs6i085gOqiiuqrJGzmf0b6/iIZJ7MXIrVHvmA7SPxZf24WL\ndnpUO72pbY8ThyD8zQU1iEOa9jFlWzjx8uISP/3pTwNw83uvtyzZs1V6Eqil/QH7oz3/Xi2tDEma\ncOq06wEchik949b4YDAkSZJOEg0OBu6c3ivGWPnc+dVFYhGArzFkZZPk0N7XZ3nuL6JT3/PbwH19\nx11nrN61ZeHQOfjjvHC+899vL8go49klK+qmFZJQWxKJvsrCtnGdqamE1fv04knqfUkMTQ98wvlg\nkqMk1jixusxILmjT6ZRszq1lA8zPOV8YR0PfA0ikfcLXGkOmxG+YirwsUUHTM1cSyAP2gkVi5faw\n0YZAzvtAh8SJu5Sm/RV299ycjccZW8KA/cb1G4yl57hGozuJuSYZZGnbW4wxh+HmsnqzPGu5JQCb\nt3ujaZ0of3h+jPc0hfWJ5bIo2RH5tklZUzfcDvZtK7L5fqb256amhR5WVeXYSoGAkJdfuQ7Azeu3\n6ImcjQ4UsVw4t/d2/Pod9AakcbOWI3o9l3CMdUgkvipNYow1vo8/SSJfNMjzmlJ4ScoOTDcMNYEk\njIIopO5IYTVn6HSa+TN0tLvnuTYKU3Ow6/yFUdCAoxX4JCBaeadVTzLGteOiePJjP8O/86u/6p4z\njtiZugJUeMQJBaXw7TuuwNH4WONbWuq6LfBYa317AcayKezsoY4ITkkioFSYJfecW8Ueo033QXNz\nA9+OM5lOUZLIC2ZTtJWe6GrCsz/zkwA8/fFniAU2nx3MfIvZ5q0bjG68SSpJpuV8xNk5SY6bJU4I\nj0kZa6w07JrKEoqDyYsZe1M3RxNycvk6YW19r4OL0Zt9V/k7kqmhlrtQXZkOTLc9SxS0C1+9sye2\n7xALv5sdQ3OP7diO7diO7diO7diO7diO7diO7QO1D7giajBSQbRWg5ADdTPL4SDlyY8/AcBTTz7O\nvjB57u9s89LLrjT95Rev8dlPu5L+ygOPgXUVx5VyiYl0044PxtzbbaooA4JIsmZ1QJa7234v7hH7\nqmyNFgmr2lYkwnw5HA6xJvRQm6oqSXuSgRhlVALnTXsJgdDJjWvFxq7LrH/t9e9xb989331zi1zo\ni4ZmUvvKgq5BSWk/CHvEkhlL5+aIBg4mqtIU60VzA4zodSpCL5z8Dtpo/vdHbcoLB1UeGqCIfH++\npWh79esWCZgGLfvv3v4eWionVe1Yxvzny+srHdDIFSmbkUhO5tqtHT733AsA/MovPEtghbHYzKil\nEl4FltG+WwPb2zvkZUUZSwVmPuOE6Lj2MsVIUkbT6YR1ae4/mIxZ33cZqtJaP86BaeEQGKfJBdLk\n7ctdAV77TrW8BcaYQyQZXcKuQ3l7DyO0nkEYq3yW9SjMYrm35fbX2t4BuVDCmo7Wlsumuu89XEi4\neOkSAI89/iD3XXDMtScX5lrWzHIPrd0aP3/fGR553FVK3/j9P+aN6w6iFcUJhXH7Zrcs/JyoKPCa\nrHEUEMt+uu/B8yytuP27sbHJ9s4+vdSN+eLiMo8JRHvQC1ACK3TzIvpyhWEsRBe7O3usCvw3L3JG\nkuW2pvb6enmR++8ThRGpVO1nswnb6w6StLJ6Dot7vmmRk/TcflyY11ghMLOEGMk+Kg1aN2WPo63Q\nhGHA0orLjoeJZjJxLQx/+ZVv8bv/4g8A2NvZ5PSSmy8z3qU/58YgCXVL2KDaLH5ZQ565MZ6McsLg\nQP6W8iQXAa66DUBl0KqpSFSeDyJNEs+WXJbGazgrYeaZSpVWB7UnhCvpEWXO/5aFZibMunfubnFD\n2hzure0SRQ7N8vBDH+bcWffdFDVSVENHmkA1hA0dlEVl/Z7FGvb2REi9mBI2DMK2QAsMKWyel8Ow\n+WatHp0p3nltmBbSSHiY9dGjFfD+xhS5JxxxbNytuN1PfPIZAL71jW/w2kvfBuDu9h6JVJWS/hxW\noLVb+2OqJffeYZISLgrRVKAxO4JoWBgwf+IUiaCEstEBe+tCLJYOGA6F/bMfUwiy4CDP2ZSK2fzC\nMjOJA7b2RgQNlG39HnUumonxgFp8k8WgBZamTYXVLRqn8VlatwzVpoMkerv9ONFDoWr1a01DXGih\nbGDwOkGL3m0YGHJBmlRaMZVzMIpjlk64KvTe3QxdN4zWMaNNV80mzz05iKkqRjJ3vf6ASipSmanp\nxS1zb1NxGo9H7GRu7VdlQT6bUggMNAw0oeh6B1GPWHygVQrb6GbPZuwKEqPe3STbcMRFZaG4u+ae\n74XXb5E1MGXdgUlZi5aDPejoOXaZhZVSmIYEryopBJEU1BbVVG+UJReCllmnYnoUZrFMxJnszWDc\nkL1ZQ2AaBEDUIhF04Im+XPVafIlqSY3qDv4tsDWF+Lavfvnr/OSnPgZAlk8YCSLEWlg67863D126\nTC92+3S8P6EUptwQ0HLuzUYTsskYI6G9CfAM6SSgZIjMtCayLWN/055RzTJqcnlCRVa49+6MLVPx\nmdlohJb1ajQE8jmpVdQ02u5QBS0pUCifP6ktFx9y5KJ/5+/9+6SCZKkmI1JBQzVMsUdlSiniqEHx\ntPveWt22CtJFHHb8sK0pBNpsrCKw4ucIOZAY5sTSCV570bURVacNJ+UsHqYhgoKltIa8ct9rfn7A\n8oqDH2ubQS1Igr17vPDV5wF47stf4e7GFmNZZ5lqNXZ7aY8kFZb+wPp2xDgIGxJg1qqMNwVBWofa\nn9OZMkRVo42rPVrDGO2huXVlsIIas3XHd+qW8I8weBtKqPOzb6n4wQF8Hzg0VzcYdTpY7U4fhdYh\nq8LsZ8qCQeogYXfv3OXFVxz7WlY6hkyAMEqYGReQ2BMPsTB2UIfV5WVev+aCxhsbB1w8I4x9xYTp\nVHoIF5ZbvHRliGUjmFAzbWi7jQELZQO/rAuszKquC1bne/L7ikpEZu9tbXLje449zGyv8dR5xyq2\nEESkEhhVgaJRcVaBIpADNYpjUoEwDebmGC66gDGdWyROGvbOsHOZaaEsXZmBH6c5ItX2MtWVjAk8\nS2jLpmsthHIorq4skaTuNQejPS92r4iYeebLDhRNq9ZBGEvcSEqEEf/iC45K+9KlS/zsx5xzq+sx\nDXolTkMGp93mPXnqFLWxzORycjAacyD08Zu7Y6KZm9+7O2N25HAoDmZM9pse0eoQNLqqWoi5eZ9j\n3oWMWWt8j2JRle15bVpsnVItxOUozFq4c9td1EcHM0wtwUptPD374kLM1fvuB+DhR+/n3Dm3lvvD\nmCa83d3d9b2WvV7Pr8E4jnj66Q8DLoHz/PPfBeDe+g6ZzPV4HJJNG4H1grpy/aWrq/OkfYHspglX\n7rsMwCOPPoxWgYd+VnVNlUtvYZG3Uj91zUR6TtbXN7lx/U3/HPdfdZ+ltWVz033/g/0IHbfucH7e\n+ZQo1CQi1B7okKmRXq5ZzdaWgxLFacJsJgfUZMRA+rcCnXR652rfU+r3yBGZ0soz337hS1/kuS//\nBQCvvPQi127cAODiYp+eyGRgCw+hnx/Ot9JCKkA1cLnSUDXMptOxD5o1NbUkyuKg9UOB0hglEGbT\ngdrYwEvbxGHoReFr6U/RAt3Upr1U6VCTSiKwxLK964LaWV5w644LdvdHE06ec0mRxz/0UeYXluQ7\naC8qbo31gaHWyrcAuRDRPch4MmZ3xwXjpip93x7W+ItmFNBhsO2M+wfUBmFsKxHheiFbiZeWZ6EN\nqqIo9OzRQdA+o6Xm7PmLAHz23/ib7G66xNB4Z5N1kWI6s7pCOnRrf3d/l4kwiw+GcyjpI7WV5XtT\nlxCw+ZR4d5tELr6mnJFPnc88f/YUiciVqUgxHbukz9beyEsIRFHEhuzB6WTM0qKbx92tdXY2XT/W\n+csP+LkoK6hlDxr1g41/t9e0y9r/YzOFl8DQVtESZ5sWutmRNAtDTSUBoTWWscCS19c2ufzhB9zn\nlFP2ROqjtJqpXDjjNPKQ1Lyq/M/9vCDtubHPi4Jx2LQpDemnLo7oDXoMSolflCY/GBNITFLMZgQD\nSY73Qn82GdUmNna2dll/y/X7FQtz1OLTD7KaF667OOz2/i5V2G6ahvlfK+0lRlDqULKgSQLqLqt8\nWXoeDVO32PrYaP/7PDvaCwxYRtKesD3ZxzTyeaoV4LJ0klS64x6sPXRWt3u2E9gr63vkX335Va5e\ndWdfPAixMzeWV+6/n0/9wmcA6C8vg7CRTw4mFMKXkU8ztu66vTzZ2wOs5+EobO3bQwKl6clZFirr\n1QuUNRRFoxJRUcvFprZwcODGdHdvz8d5dVXRyY13OCSgbCCgRhFWArnXigMJyg7iiK0Nl9T8H/7x\n7/I35budXlokls9/c2ObozRFK7eH1od8gOpcmjw01XSh4vhET1lb6gZDbzX7wvp8/uJF1m65fXDt\n2jWoXeJgeWne75ukl1LFbZK34aCwnZaK1bOn+OjP/ZQbj8cfYH1ti2u3XI/od6+9QSbxTKJCrOyp\noir8nSrRMBLJsTf2t9iRxIyuWtZ3EzgeCXDz7i+idYfd21jfd4217WEeQKep9lBSts0vtS0lP0wL\n0jE099iO7diO7diO7diO7diO7diO7dg+UPtAK6Kq8weVqT1Jhup0cwc6IJTMa1lbvvSVbwDwB3/w\nR0yzhjgm4c3XHevUR5/5LJPSlcIny4+zooVBrs55/IlPAPDySy+ztukajpf6EbVAzvppxkIibJxR\nW01UxtJLG8IRR6jRZL/DIMJIaXs+iTCib5hVNRvyfF/8znVu3nBi0jERZ0PJgunSl+qVCjy7FCgv\nghtEIbFA3+J+n7jvstNJf45IMpk6CNq3ug8DXDaiya78eE21UkydxHJVVR4W5yBR7mdNCwfqDxOs\nwE7KqibPm8bpvGXUVAHaE4VY39wfJQF9rxmWILJq/C+/9a+ZH/wiAB+//wyLUh0KtMY0REoo8rL0\nmnRaKUIZ87SfUgsj4ayoubfpMnazrKCeHsgXalnCjDFeZ9LUbfbR2rzNtL8Ha65fZx1mzKqqSAV6\nEYWRzz6absP3ESfxrVXs7LisXl0ZAslwrawu8vD9rnJy36VznDjlqqCDQUycCFwxaDOIdW2wttH8\nbKFKeW6JBE7+8z//KT75SQc9unbjNt/4utMKfvE7r7C768b+5ewmI8nyPvjAec6dc2x8i0FA1FSg\ngwo8B5wbn2adGRMwkerq5tY2N99ye/Da9evs77pqwkc/+iFOnnSfmySB3+ehjigF1p/2eixIRRQU\nt287ZEVRVESB7MFszMKSI/CYGw4pG+E8VXZgyhVasv7b2zv0By3By1GaMYbP//EfAfA7v/973Ljm\ndNgmB/ue1Gl+fp6+wC8D0xI5xXHsyRVqY1oSLpQndcqzMbZ246rqCiPV1LTX8xnfINCeDCnQGiWf\nWVG1qVGjvC5vEAWOwVgqk7Mq90yPKsAzwBrweqG3795jIqgxGySsnnKZ56XVU4SCnLFB6CGMtW2Z\nRJTSHXdrycRvb2yts7svMEdb++yyrWpMJfs6CjsY5M4+fhfI549i70asozo/vZP+pbXW6ymC8f4j\nCsMWUUFbEfzoE0/x6kvfAeDFb3yVUsbjztoap0RbV0UpI4HjLVrlK2lxb8gp2ePPv/hd9jbXaTbh\nMI34yEOuirewukyUiLh7VTEWpuxRXjEnmqLbO7tsrLnq6umTp+jLfswmB/zln7g1ff/Dt/z8njx3\nibllR8JT2wgjkETHFNphcuz42HezHxeJkVKaUAgRqasWMl2DbXQZ69ITIEaBppYKhy2N105/7lvf\n5uNPPArAwsoqfdHcvn1vg1TQDaPxjKjxYUqzJUR70WjsKx/DwZChQGuDIPDMuqFSrCw6X7iztUWg\nQpRuUAmwICiDdGEJI/t5fzxhJASSd9bWqWRDbheGqey7F2/e4muvfw9w1TC/SgVdBg7FoTpV/obZ\nVaEOxTAehVTV0N3XMnW1NdR5g8Roz4WjMGst46nzPTm2ZcTV3cKQ9b5Bae10t3GokGY5Wtu+11jb\nxkwKgoZQr6j4+tdcq9EzP/2M93/zSwsUUnkz+axlSLaWROJEU9bYxndiUXU7nhZFIZWxQHUJeWqa\nypXBePKrurbUApUfTzI2JG7Oq4o5qbDvZgWhVHgLTKs3jaJsWMptSCXtZnkasyOu6V4N+9vuvH/+\n//wtfu/zfwrA3/3bv8icIJK+d3ftvablfVnXZx7+uW0V8Su1E2dZa3zMaFXggdVKWe6sOUTUtMh4\n5tlnAfjCFyZsrDskB1XBovjSIi+pmjbAICGXMzSvSrTExr1BjwuLLqa4eOoMo70Rj9znxv/yqbN8\n8zsvAXB3Y7OFFJcVldxHltI+14WA7tr2DqVom4eV8tBoEyis8RCNtjps2njOmpagSAEe+qZokYrW\nHq6I+gGjvRT8EP71A7+IJg3sItCe4SmKAwL5OZvNeOW77pL5l3/5db79bXdYah3711PXfO95JwCd\n/60dGLpAedxbQHRemay/zgnxFk8++gAvveww3NfGM5RcQPIixi47Jz7fT2hwYwpDIJecuqxJVYoV\nRs2qhj25nNRlRSkTs5NZ/vBbDo7y0q7i537l1wF4+V/8M/TOWwBYXXsYXGQiTNgGbqGIMUdJTCTw\nibiXEorUTJT2WzZdrQ8tge795N1Euo/Wur2Qh/92C6NV3Vf7ftgkCRk30aQO/cIuq6rd8Nb4ta9R\nHqrc6ye+n7ioKnLZUG+ubfI7f/RFAB7+9V8hlE06iKxnAS2rmiwvmMmhNctmHEjf4PbejO1tN6cb\na9vsyeVMGeWhbdZaD18pytoflla1FN1VVR2SXPirZqA7R1VVYQW6kaQpCJNnURZ+wx+1kIu1ltHE\nfe/BIObB+x0E95EHL3NixV3Eemnq+xH6/cQzQ2vdQkrm5pZaCFVV+SB4NBpRCrRvfn7IvFzcnnzi\nQR5+2EGp//APF/j8F74CwDgzvHLN7ZXN3V2uXHAQ/Yfvv8LyktvjvV5KoBs4PxhbIX6Y0bjgngS1\n12+8yc033WdVteHkiuu1WlxcpEGEnTy5BALz29kckfYb1lbFRCBQeZ6TyXdI0z59YYCNQkujn27t\nlFQuZASR3wPTyYxSLnNahwTiQzzM84hsfLDPH/+rfwnA9r17xLJeZmULvxqPxswymdOe9kGgsZZ+\nKr4nDJDHZZKXGOmfzbKJZxHXWBrMfY0hsZI8iSO0BGS1URj5/F4UYz1a3/pArbI1FTVaetfCKvKB\nJrVFBQ30qGJ7x7Ey7uweQCyQ6bTP2UuXAVhYOeH9s3NL7T5pkgI1bX9nVdeMZw7auLG9QS69iNja\nX7K1qqlL6SkNg0MM795+CFbAH9X837XGJzeMMS2MUeuONEMXgso7nhWD+QU+/bMOFnfr+hvIfZGN\nu3e49pZLvMzNzxMIRv9gVtCXpO0gjrh80u3N8PFHubex7fuA5/sJp0+5y42xNbWcwZkx7ArkNKvx\nEMPx/h4nViXhND+gyWTO92LWb7tWnO/u3OFAzoy9Aq485hJavcVVLl9xCbMzZ874i7i19pBM3Dud\nhYfbIo7Yryqwct4ptJeF0LXxCSBblz5Z4xg9nY81JsMKLP3a5iavvelaCp64ctan366eu8DmroNS\n37l3h5kwbQ6GUMghOi3GhDInLkEt8VWWMZXLVRSGJFED4bT05+YZi0TecDjENmzGRclMnOxoOuOu\n+Njbt+8yFMmR9Y1tbuy5s+Trr19nc+TmlyDxF0RrrU92dqGQWENtGz/Z+kbVkdGwxvhxrJXxcElt\nLZUkAfvh0SbhrW37TivaukEE7UZStd9f1tZtv32n3SwM2+JCaQHV9mA2yzAkYO3epnxOyIEwV9+8\ndZOo79bG4sKCb3HSGkppBdvb2qMQqY44DMhNC7m0QURIC5v3l0Zl22SfqclEOivLC2qJ3TY29zw0\nf2VlFSuFliov/AXaanwBQVlLKD2URRqT912yZFNZNhopNRX4cyKMErZHbi3+xbdfZnHR+fZpfrTs\nx10PeIiXo65bxRbClveElu3eVIay6e9VEPiefMPatpuvjZ0Nzj3k4ouf/vRP8+2vfRmAva11z1Bt\n0GxN3JkzrSyDeefzLl6+zFB4Y5SpiJoL/rRgY23LJ9PXd3bZm7i4dDsbe4ktasOcMCxbpbgle7Os\nDJ0elVbiJVRoSTRYYzoxpe1cMlvoOQp/fjuK6GaFa+gef92414/pDy7fcgzNPbZjO7ZjO7ZjO7Zj\nO7ZjO7ZjO7YP1D5wsqImCxjFMaVUmL737e/w6quvA3Dz2m2vFad06Al6QBGI6nGA8eyVL3/zOZ79\n+V8GYK9QDJcdLGgYpEzufQuAs33NQw8/AsBLN9aYSKb/9RtrrG24DOD9F89wcjG3byoAACAASURB\nVMFlcOLK4jkrSoMK8ZCyajKlajQJq4IDyS78wdev8y3XL84v/cf/kEefcMLh88PTfOUf/TcADIo1\n4qaaoCAQ6EIYBERS7YySHkkizxH1CBroWxh6QiOlNPadyuL8uCuhzd8APETh7f/X5wcP/TaMG4hs\n7RM1SRxTSRbKIUskc6qcLiE4wW7VZHYx1JJZq+sSLePXHwwwksWLk9jDzLamY0zThJ+XzLKCiZAA\n7Ozvsy2N+Gs7Y9akgb6a5MzJmGsFMykRTYuyhTMmMUgFJi8MmWhiWnMo9/ZeQ/h9r3JakO65+/0+\nWrQTjak9wUOHJPNozBpWlxys/f6rD3DqlKtyrCz36ffdfKX9HrGU/sK4ZUUOAt1CqEyrh6p1QJoK\n5CyKyCZuvLc3N8iyiXxm6tf4Zz7zSXoCG/v9P/pTMhFPXt8ecSAZ9ls311kSiMvy8hIO7duwWOee\n8Gp/NGN3371nNJr4CtGJkye5csURQSwuzrVzpAI/nLv7O5yec2LSeT4j7QkqIRkwGAgMbjRjJDCp\nPB8xP3RzdObUSXLJHO/vjryPC4OYuTlhwO4lhELM04ikH5Xt7e3y1k2HxlBALJOUBhFGqoyT2Yx7\ndx1kaO78CULdoCsCXy0ZDHsgDItpUfn5VdaQCWS6mE19lbWm1c2NTeJF5JTShFJZDaKYWPZKEIWE\ngfu7VVWCqQnkTVFao2Xui8L4iu14NmZ9yzlWHQWUMmOnz5zhkUcdEVZ/MO9bAmxbgHJEIk2q1ViP\nXDg42GUma9FVZgQ1UVQIupggCFEN0rO2HlJ5SBvtx+xr26pdWxlyLMAC56sqtG4IVLRHbISBIk1i\n/95DMFXb5p5XTzqI6/zCIvmBg3RdOn+etbsOcnbzzi16svaDIKKXCmLAwlzkPvPk4jyUpT8vT60u\ne7TCuMgx8ny7s5KdsVtDk8oyOXD79MzKIieW3d4OO2zd88ME4TbClDPuO+cqDrvjnC/98b8C4O64\n8qRA9129yrlzjjn5wQcf5OrVqwAsLy97YpCqqt6RrOjoz0zl9xGadp1o7dFYyrZzqgLtyVR6ScKq\nkHAN5hb4/Fe+DsCphZ/hqvhqPZuxePEK4EAEL73q0F5rOzuMpNpZ1jUTQf+oMKE35967sbPtq6CD\nXkophDJRoImSmJXUnQHTbMrevqvKVXaXzZHzezvTKWtCjDMbTxlXbpLujsZ8945bN3tZSSCMu470\nRSrCWN8GoGwLbbfWHEL7NOteK+U3s6lq36ZibWcZm9pXXAdR8h5z8j7M2lZD+tDvu1GOolM/8vGQ\nk2hsiJlU6zcsLQt+Z29iFVaqZ8tzi5xcdPPw+S//CS+/4lot7rt6hSuX3dqY6ydoYQuO0NRyBs7G\nE6bjCYNho/uqCAXRVOYVuXyfCuvbi8q6ZGffxcF5UXEgSgGzrOL0SXcm9no9tvak2maN3+9h7fnn\nKAJpYwCmvYg1Yd890JpaWqRioxDOPuZOnObMRXcuh/0eO7JeG2b1ozRfBe20jNWmRfbZWnkiorqG\num7IEDWVzJdjQpZ9XQdM5Juv7e6TSeV8cWmBR0RT/dUXK7ZFlWBvb58t0fXc2Bsh3Jh855XvMhQk\nVhgoSmlby6Y5O7v77Aqh5rSsfBvbLCvICrfPh3FET9qIJkXmGeaV1Z1uEosHKBp8rOw2kewprEdr\nOjLShpzPYjuVYjprt8tG1EWYtF1p+gcOVz/Qi6hBsb7nBva1rz7PyyLHsrG+4VFZSsdeKD4MQw7V\nf6V/oQ5bSOc3vvSv+fRnft79PlpgLXMDePXEA+S5O+zu7V3n9LyDuD5y0bCx7RzsbjZgU/odvvzK\n65xfdM76gTPn6DcMpmWB0jXNUB0cZMwy90zbRcFXb7mFdsuc5Zf/w/8IgHRlma0d9xwXnvlFNjcc\nRPCN3/pHLOAW0Cw19JQLZIMgIhDoRtIfkPTcwoqjgQ9eQ628A1OEXr4FU3tZkS4r4I/bDomqd1gJ\n1aFF6yzQyq/Og/EBVvoXBkmKlSBBmZmH6mRl4dd4VRYo2ZxxErVMg2mMFTr1bJYzLz1EPWtIh25s\nskHIdEMYN6czdncOWNsSEfesYCRU9bNZRl+8Y7Q0R141jJoTKpH6CaOYZCDOCUVWtnBZRdNnmDU3\nalCm3czvMSV+A1tLJfAYE4eehTYKNFYc5FEDAbVSPHa/C9iMiRlIMiRNEmK5/MeRIgqbNYjvL1JK\neWmS0Wjk4V6gSAUqkiSJZ5y9ePGiv/BUdeH7pqDik099yL/3X/5rx/g6y2umgtDJ8hF3d9zhqK/f\nJokCz+obqFZ0uzKavG6g2JpIgrvTqyd57DEXuK2uLHiIbFFYChnzdNhjXw7jpeU5EsEqahUwk1Mj\niVOmksi4e2eDYtXBhZUeeQHxPMsxsr5XV1eafAVWlZ5GX/2AbJ8/qJV1SR3IRamESHrPBv0hE1k7\ns2LC5rbzvUvJkDOr8ixBTGUaRvCKvvS2RWFEKn64F4Rs7cqBOhoxGQtsvKooJFGT9gfUImEThhFB\nJVJA5PR6biyTOGiDbxWggggtDI1hGGIlsMIaZrI3t3d3ffAUJKmXGXnq6ad5+DG3boIo8eej0UiT\nKa4HxgeGNUUhCcTZiEo+P40jQt1AlRTWyLwHAbphVM9qgqDxvW1grY78+Ox8ttLg/b32LI7oirpp\nR1baj2dtQQVN8FRgBHJuTZv8xXZ721p/UtuaMnPjMUwTzp5Ykf9jeP3mDcD5yCbxVy8uUA0l2RRG\nxPMDP4ZFGJFLv31eWsby3G/c3WFL1g0K7lt2gfL5Uys+cVVXFbaBQBtFLC0ZW7sTTshEXlpd4OZJ\nJ2l2e7RBKS0ML730Es8/76QPtNasrLjvcP/99/PhD7uExUMPPcRJuXzHcewTEO8m7/L+TWFNJxHQ\nuPigjd1MXaNp4O6K/ryLPVaWlynkcliUmjfWXXzyjz/3F/zaX3d9aI+tzlHK+fPko4+hJUH/3PPf\n4t6O6ymb5YadA7lo6JSRnGn9RHH+RNsjbyqRX1EaVRufeKi0JhPo53g04i1hWF7bHbEvfiSqQyLl\nvuf6JOeaSMooGxI1PWYKD8cFxyLsBsNBt+VHOr/2F1RlQXl/Xnj4Llb7nrdYWXry+9WFuXebkPdl\n1hofkzg4brMf8XInykatH7DG+/6qKv17w7jGyK2gogvf7UDosR42u7W9yS//4q+4vzuf8kdyJn7v\nT77MRx53Me19F86gc7c2yAvKqW8Ed72PwnegygLVKWbUcvnMq5KDmTuzN7e3OBBuhTwvCeXQOnv6\nDD253GsDI/H1W7bmRHPGlBW5OJW9RDMTmOm5Rx7ks3/j5wB47KNPcGLJXaxvXrvO//tP/wngLoJa\nni3PSwKBEzcX5KMz652dtbrj99p2p1rNMMbF41WVUJVNq9HMz5g12rePGa0YCTx2bXeXSpIhZQmr\np9zlffXMNts7LqaYZTlbDUN/NmMmH7S2uUnleVIsmW2gvJaqNg5ii7sE64aSGEUoicB5lRCICsg0\n0F6CjxJUw/yr8XcnZWyr+tE5D6yGRjQB3eEhUG1yxSqND2aVwnYSMG9P1Hz/L9/bjqG5x3Zsx3Zs\nx3Zsx3Zsx3Zsx3Zsx/aB2gdaEd3bO+B//z9cNmQynvmKURgMPFyzy+PRZbgC/A1cK0sqxEW3rr/O\nq992ENynfuqz7Iq+1sZknpMXnnR/l4B+7WAjJxd7pML8OZhMWTnpqo9740W2txwBwFffvEGPofxN\nQ2UtmWQtylKTC3RgYzKinnPZxZ966q+xesJlfbbzCQtDl23a36t46rP/HgDPf/U7vPatzwFwdgns\nvBB1DAYEkhGNF+eJhi4rqfspgQgY2yCWjIQIEMuYdOT3/n9hh4v4LcSmSbYEFs/eqXVCIZWTtJ/6\nhvm4VgRS8Q2jlNBDBK2HSJdlm9GzZcVA4AlBGPiG6iRJSAVuuryywtlpwdxtp/d0884d1EjIokKF\n8GgwySBrGGsVnlk3iEMCgZ3ktfG6stYaX5GOo5BAqjfdGqay6l01lbq/bjJ14+mUOWFIHPT7NBi1\nqjYUR1kXVQ4GDFDXka82RVHkIVRd8ghjW/ZorTW5wFG+9rWvcf78eQCWlpb8e7IsoxAY+7A/YDgn\nxFtRckhwfiLV1I88/gi5wAu/8Gdf9pBbtPaalko5ht+GOCLQ2iMFAq1bGIm1hPI35oc9Tog28dLy\nPLNpQ0RUMhLImakNQxnz2bRAIezMvRAjlbqDgx20F6q3pMIIPOwnnhXamiG5QKyV0h460GWYC8Oj\ndbvWgPYMjdprrCZp6CFDti7JxX++ubnFUFjdVs4ukkpVoywtRpj2kjQlDJu5Djy0PggjtoQ8aDqd\nepbipNdvYchp6pnPZ7OcmWRsB4MhUSJwsob5uxGG19oxcADTvGRTUCtbOwcQCHIkmuPRxz4CwCee\n+UmGAjesbe3JYbrMfofgl6aFolljiWXujKk8E2UUtmzdWuOr3FoHnmzNDWEDNfxx5HHfwZl3/JC1\nBiO+RwWtJl6W5ySCYlA68IypRVm1a1YrTx6krCGSOS2rkh2B5qbxMqnsmxPLC8SJa3W5fv0m333R\nVRwPzl3g7GknyN5LU6wxNFKR0+mYaenWxEE5Y3Pbfe7G1hahYJ3Pnz7B+VMOajvo9ZppJ9Casmxh\nxP3QrcvITqkyQS7MzxPJ2qrLyq+Zrhlj2NhwFby7d+/y3HPPAc43Xb58GYD77rvPQ3mbfx+VOUIU\nqUgpPHzaWONhy5ZaoH6wuLDIwsKCf3+D0ogIaKhwrt3d5n/9/S8A8Lc+8zN8/KKr7IZVxlOPuLaj\npfk5vvq8Y9Z84807bO063/bKK6+ztuHOx9Ori55QJon7WKmc9aMUbS172w75UJqanUZDcnfC+rqr\nxO3tjSjH7j2nVpaZk3Xwxb/4c69hGuvQn3e1tof0do1nbX3bOdZUio05VKlu1nFtWqInjCFsoMw2\nYE4q9Utylh2VWWt9VfPtx7cHFugW4WLKCi1tC6GxbYUJQ10Ku3NV+DJUra3Xbc7jkOFlR7z1vXyf\nceHO/Gc/9CEuX3kYgP/+f/qfeeG7Tov7zVu3WBK96qVeynzYl+fRlGVJIWMVRREqb4iIcqaCfBhN\nJuwIW3heFKCFETuMWZJzepCmBE07SV1zS957q6rQsVQPw4BMvk+0ssLf/vW/D8Cv/Qd/j+WTZ/14\nvf4dx5L/7RdebNl+UUzF95Zl6QnGDgSleFRmLRRVu45q25DWtTrTBoNp2r4q16YBTr8WQTcEJMxs\ngzLIWYrc+XNl6RQUUrkMjItBgStX72NNyINef+MNqpZ+l6rM5XlqjLSkVVXV4pwt2Lom8JV3Oqgf\nixKfMp6OiQM390WUen+oQ9PG4qoDfVeAQKYt7i4FHGKqJ1CgG0QHvk1G6S5MV/n7yOGxtm3R1Pzg\nZEUf6EU0z3Os9FSGUUJbkNWtKLBqWe7eTq/uMcwoAoF0mXzE5//g9wB44hN/jVAisoNZRjBwznr5\nylPs33MbQWW3WU7cJk9M6FnRTi0PmCy4YHVWleS12yA1Fh0ljLMGn215655z0AtLJwjk0thLQgph\n0x0OemgRd59NS6pFd9B99jf+If/tP3AswNvXv0s6cJ+T9iJOnFoG4AGdMn/awQh7i6skIvWgg6DD\niNuBwL5Lr8uPi5q+seb8t9bJd7jfqUOYXU93DjSYhtX5RZYW3HfK85odCVK39kc+aB6Egb8UobT/\nrtoaKrmoZNMpkTR0nTq5zFMfds461Nb3j9RKEUYtlLQ3TLgqsiSrZ5a5cetNAG7cvON7xiwWKzAG\nGyhSgSrGaeIhDXVR0jaQWc9kF4cBadMLW9XUtXcF79rT2w6o6rAIWiYT9zyLc/PMCVV/VhYUAkU7\nErOQCBzXkvoDIgxDD4F+ezKoMWOMhw8vLy/7wG82m/newrIsyYUdc9Dvech9HCdewiSKIu97oyDi\nJz7qgqqFYcqf/sVfAnB7fbPDAKcxKKpmmVn8hVPXVQvxV4qe9HnWZc5ELvNzc3P+QLh75x7DoUg/\nTcbsyAUrzwtOCYRPB3jZlTgJPIzmzOmT/jKzu73NZOoSYItLS6yecO+NwuCQ2Hlzkhw1FFChiKXX\nsjTGS0HpKCIV1u3xtCSXZ9kpMl5507HxLawMefrDD7nnDUKMp3ZvGR3jOGJJAmWF9tCtjZ09tqWP\ndzbbZSri773+wEsRJXGPJGkOWkvUcwe5xhKFAaEcfmEQMJIEwb2tbe6JbEBWKhDJnCv3P87P/Q0n\n03Tx0hUQeJiL9pvHNtiq6QeyhxIpbaueoi/w8TQKvfRBqPHyFaau3C+AMNTEwizs9oX4I+UjzSOz\n1od3YbrKJ221Vr4Htvv6uqrI5XFCHRDKgBRV7fdvEofeN0ZKM5b5UmWBkovBLJ95dvo4CFkUaP3D\nVy9zR4KqN9+6we17LrG7MJxz8Nymrx5FKYHV/mzsGTUHcczJRbcWL59cpSe+Jooif+GvqrZHVGvt\nPzOOIt8nT6CZShBHFKA6ElLv5KeCzrm5u7vLliSbv/nNb3o/1T/iC0wDtHQ/tRhordrxt8CZsy5Q\nX1xY8AkxCx6CasoZyKWxMgE399z++Ed/8AUOnnVw42c/9BCLoRvLK2cvMOy7fXrp/C1efs3Bqr/z\n6suMdtylo55NyA+crxrt7rEi7OjaQKCUb72YVYVPBm1u7LG16S6o1Iqzqy6J8PEnnuKOnMd1nKDE\nL5gaD/evdRvfatWBKR8KUW3LLKzUoaRd00/vLimyN22b2FNK+f7aoyaxNrZlEn37ympCbK1Nx69o\nrGq4MIzvEMBojCRYKLU/owqtWTjjxvLCIw9yU6DQu0lKsuLaPvLNe1w86y77n3rmaT73J38OwHiW\n+UvcbqAZyMKyMrJN7BUErUygtS1M3xjjCztpf0ApBYFIBfQbvhKtPVy4DBXjyPnhNat8Muxs2ufE\nObeO//5/+Z/ymb/zd91YoDz0+vOf+yP+yT/+fwAYHexhaGLpnKJqEpEBB9I3vrOz/Y7z8X7NWsjL\nhtHd+mpXbRSmdnvfFD3qwHEo5HZCYd34R8Ez5AfCPH/vNQ+DV1HFL/677iz62EM/gS3cXtG9ACtz\nkfSHPPHxjwNQWstzz4k6wM4WibSSBRY/ltTWs+znRUGWl57XwNKyohvd3ldrqymlMLZfVpRZo8QR\noMSPK23bBFBn57mOuQZrzKH2lqpREAlUSxetaX9WzZu+b7Tb5HsdHNrl72XH0NxjO7ZjO7ZjO7Zj\nO7ZjO7ZjO7Zj+0Dtg2XNVQodvktlyFdEVefnwy/xzbHKEpiGIRBef9lBhv7yuS/xzKd/GoC98Zjx\nSEhTlhaYO+sgXWvbSxSb7vVziSYSBilVlKSSKaksDBrym/k5bJhwMHUZgvXNMZGSyt1ciIqFyERn\nlNI83l8e0NStrDHUUo25+MiD/PKv/wMA/q//4j+jFKKAvYOCewcOMvry9bt8+etOR/XRRx/l6gOu\nOnrp0iXOSQY1jWPP8GVqc2ig3okV8MhNtZBNV93pMGB0XtOkP60xRJIe7McpiVS3SkrGoo10MD4g\nkWriXJoSCPFJYSoqqQJWWY6VjNHZE8t86mNO9/LZZ57lsUsua6jMiKYQWSpQUjUNAotWjnEZYG4+\n5YH7XHV0fm6O/g1X0bt2a5uD3DWYq1CThE0VL6KQNadr5SuGQQi2ySYaxXDgsoZBVfmG96KsyTvV\nzq61ZAX4SlYvScikkjidTFmWapQOAvaPsiKqhAEYsMRtZpnDUFKvDdvR4OpWGp5++ulDcCrjWX4V\nlcCCiizn1q07ALz88qvcFYbFCxcucv6Cm7sTK4u+Qnv1/Cniz3wagH/+rz7PlhBh6NBl1RtYpFUB\nttHE7OT7tNYsCMRodXGeHRF6z/Pc6bTiKiENBCfQEbHAkCbjgju33XpYXp1nYdH5gjQNqcR/zc8N\nPBRSa8XWtnue8cGBr/wuLMx7xmHVoTw2pslnHpFZSyWMg8Zqj1YoTU1v4Cq+ua2YiXZmGqdsy777\n9suvcn7Vkb88et9lImG1xSpP0GNt7eGQ83NzBA1kMukTSDVma2eXiVQ0Dw4mRPKaJEkJ5edev0ci\n+yMKNVEYEEvWtiwK1kQI/Na9e+zuu3VT1ooLwhD6mZ//JT704ScAp8uaCxSw1tpXmgw1WmBFWrVM\niFmWecijAuqm0lKWjkwNiIPAc43VVem1SYNAE0UtvLVBaLyjoPePYJbWPVhrPLmXNcZnwF1hXTLd\nHb9vrEV7nUVLJR8UBtrDG2MNRsbsOy+/yOd+57fd68dTkoYZMs+opaoTKUvSJMPjiHOnXaU/GfTY\nFNjntJgRmpzKypijSWQfrfZTVCAaxGmPEwuu+jYfp4RSMa9p/UUQaEzVVHJClCAOgl7imdOnpmBj\n5HxBESi01+nV76gNGgTBoTPRM7Jq7aGADfrk6Mz6ahBW0RxIWmkPkz578axvBSjL0kPt8izzGrpV\nWXSItCylrNODMuP//hOnm33t3gb/1l97BoArJxZZXXavSXsx84Kkme8FjATxMx6PPDvr9r01djdd\nlZvKEOnA+8ZxPmV/3JxBOZGsjzNnT/PQQw5BMVxOufeq27Nhr8/KCefH9+5u+pEwyrT7RH1/TCfD\ndagi0pyQVV1TV62mpAeBAbJMyLRl37h9vb67/w4f/v7NGusr8UHnuaCtiFJX/vwplfJar4Sg5qTF\nqtdDCXSzspqe7IOzly+QrjjG6Lt7Y97acFW1/+S//k1WrzoU3e39DfqV89uPP3iJb37TrZnROPPn\ntQZKqfhVopvrkQV17ZUXojgm8Gyo5hBaJBQN87k0YShoEW01RVMRTlJ+9rO/BMAL126wvufmuC4r\nfuM3fxOAz/6dX/UV1KrO+d3f/mcA/M5v/1PPmG/KmlxabmrrkGYAk2mrb9sXFtmjMmstRdkyLjfE\nPZUBGj1OY8hzh0oszGnCniPCC+PHqI2LBez1Tcq91wD4N//6x/iNX3ZqHWldMO7ohTdrvKorekM3\n15/4yWc9k/dLr73OtVsu3t8fzaBBiQYBM7lDjMZj9kdjCq9DqhBUMHlZM5PnDqxtK6LTjEyqt2jt\nY984jbz+u9IKI4R81Ma3xlhtCROp5mvIE4kztWrbJYMWeXSYTbc72PhD7Ie5gXygF1FF96rS/pe7\nezb9n6oT+OpDR33Dd6WUpdaNpEeIFbmOz/2z/40nn3rKvTbqMRP2viJJyOZcAFSdvY83Izf4i2+9\nwUl5bxnMqATqVY4rNoWtTkf7xL0+mXi+g1HOIJXS+1yfmQSyiZmyMBBceZSSSe9UWFnCQOAN44xP\nf/ZvAfDCF5/n+T/+LQDiRBEI8yomYm3dHbR37v0pyIEzGKRcFfHun/jkx3nySXexXl5YaCe+C9N9\nlzk4CtM0MgJSnO/AbRpIiDJtP4hR0Kz9iopR5sZ/c2/ErgQ0sUpYEHmAxFqsBIr9sgCB7y6uDPnM\nz7tD92Mffoirq87RJ3FKqNyhqbRCy7KOq7YfTwXWwe3E+arK+L7VQS/l1GkHxchsTCaBQzyeEivp\n0bV49trAai+9o5VCx9IbEyaupwDo6ZhKYBxRYugJiqEsSyph1quN9bBmY/G9NMM4ptf0XE1mjDN3\nEDXQ2aO0tvfM0vB9V5Xx46a1pQGCOFbm5p3KH2RVVflAPQxDavl+Bwdt0BPqgMuX3Po9dfIUm5sO\nIhdFEakIdodB4NlBq7L2LIj/9i/8Df7sS18D4I1r17HgYXXW4mGLxtYgkP0kUlyUC+6TT32EWCDW\nG1tblGXDzpoSyEVofn6JmVCib+6s05NAeWV5wdOgZ1nmkwXzc3P0vbRFQCrw3c2tXd8XdzAeefbO\n/qDne50b5t2jsm6AHYWRh3jXpiaMJaHWX/Ww27o88P5h7WD8/7V3psGWXdV9/+29zzl3ePeNPXdr\nHpAAA5IssC2wASMUgU0hOxUylGNsJ3GlEjv2h1TFValKufLJTlWcqRIT7JjClImNMZPtGBswMggs\nkACBJjQL9Ty/+d4z7Z0Pe519zmu65W70/PSqvf9VLd2+fd87+5519tp7r/Vf/8UXHnxIxjjN9ddd\n0/xSeuKTE61CjVOWGBjInNJT4aA9Oz3DmbPe3y6eOc26bDxWxmPWmnobpwIN09qKfpaC1KgVk3Go\nCR7nFZXz9ppe2M+tt/8AAK9+7S2ozPuIwtZoWTg9famppVHUoYShsyEDBo1Cd7/HoSM+MHH2zAlU\nY2BtGUtjeOPaVh9KVUFXIE2Slp2kz7er/t6h8IcPgDTNQv2O1q5tmVO1NYcmSdsWGLYMpSvWEuoP\n+4MMKzWbf/LpP+Evv/A5ANaOPMv180KP12moobIaRqapcNfhgJQYxVAChXP9jKEorxZFjlaKXtYo\n3afopkVZmYd65eFwwCC0geqRNjXo1rb2UgYl827iclGrh6Gx9OUgejJPOLksdinrlq5m3Yb1r/FN\nzrlA7+weVq21Gw6rmwrn6wXBbyAb2unMzAxXXulr6dNUsy6BG3DBr9a1I2vuQb6GkhIQk+jQvsRa\nRZX54ORfPfocT4ruwXvuejNverU/JM5YxbXX+mvt3DnLitBxT548xdqkqdke4oTm7KwlMYbFRX8Y\nsqctI9HPqJUO9cRX7znAwoK3/VgrnjvpD6Kr4zG7xNfl6xPWV4SqeDF7ko7/MkqRNHYpS5SsJaaq\nQ4BZKcjFb9RFRS57s6dPnGQzoZxDKqxwgJWTwEQpyqQ53A1Ip7y91NSQVAJtup+xJs94aUxo0TE7\nPU0iZUcFjjNS0vGdU6fYLWqrt932+rCfsf1ZJqV/fe2Bq3jlDa8A4P6HH/XPFpCg0VnD97XURRme\naa00QaHe5bR8YRVa5KVphmqChVk/BNoqZ1mTNeDAK17JzA2eZnr1tTfxzYe8/zxR5fz6//zvAPR2\n7+DH7/YdLD71yY/zRx/1B9GimIRymCIvQ5lSr9ejFmpumecMJQiy2X7VkjeKKAAAIABJREFUOkvR\nRC7QIAkFZ9tnKqkMhfHlXVny95iz/j6vza4z0NIGaf+ref0rvI3+63/85+zaJUHORYtebxMNRlqj\nWadD3anJUg7I3L/q2mtZXffP0qFDx3ni8acAOHz0OIn4yOnpBfr9s5w96+dRWddMmrlgDf3mRNiD\nUqKwZVWHijFXueAb84ntVEGqoEyucUHl2c9Beaan++gmgJ6CTWUSmA5/l7Y2GudaFWmlcbZNDFws\nIjU3IiIiIiIiIiIiIiIiYkuxtdRcOnSmbjNUFBuFBkKKbQOXIxTW4qhp+tRpEmkq+MLTj/Clz/uI\n75vveQ+LoqC7vrpGr0k79xJmFjylc2IGnDjioxG7z5zA5j76QM+wc4dP05f5hJWVFUo55feTHj2h\nSq6gSVKfcUhUTapFlTFJSIWSVGU1SqKJRV7Qk8jYPT/zczz5jc8DMF55gcQ12bYeRjKzOnEoyQwU\necUjj3rK7sOPPsyBAz7b87a3voXbJQs8HA6oytBkbgMNabPRCBBY2gbynnwo0RBFaKJrnWVVVASP\nrSyxIAIM/TMT9oqCSp+E0Zq/f1NK0ZeeSWmumBl5W9zzUz/J9bdd47+TGodMs+lGanQaoivGqU5z\nZItTmla/14YoDs4GpYMkgR3ST3b3wjyjvn+9srrOoeOexrS2VoT+adPDAWPrMw66qlrhHW2CoI51\ntmGDoPSw7f1X15RNX6+iIpfM0dm1lVaRNUvIJCM8sJscuYcgTFJbFagWzrWZWmNsaPrsn6O2QD00\n7NYEtcuyLDl06JD8rKEcSzZ7Zpa+RDy1hiuv3B8+E8Q5rH/OAdbHq0yEMrd/107eceePAPDV+RHf\nfORxxnLflEmoG8q0suzc6e31qhuv5qYbrgGgPzQMp4VulO4Iqrb9fj9Ejk+ePMGJ0z5Lm2SKXbvn\n5VvWobek0Sk6k8h9llA1GYoatGSQB1ND9KKniC0tLwdbT+fTrVru5gZ8QUHdNHuvihDZNDqlDkI8\nmqmBv//r1Qq2ETci4dkjPoP7uS99jWTgfdv1V+2jFMpbqnWY187aQP0d9AyJqDUO+yPmR9J/d36O\ndaEBL0/GLE28TdfKimXJuJw4fowTaydJGoEdowKlFtdDC0V4/4GruPX7fSR+NDOLbbLfmpAN9MJo\n/hfVtQ2UyyQxYb2ZVAUri8I0eeFZHvrKlwA4cvA71JWfv9aV1DQlHy70auv1DFPSH2846AUq7/kU\nW18K8jwPYjr79u2jySEdPPQCe/d4f290GhgwaysrQWxrMp4wFunvwWjIcOSzLk8+9igf/r3fBeCL\nX7w3PHp33XIDe4R2cWZtEhRPS1eBZOQ6AogYoxkIDVs5xySXtS7VG9qc0xEb7A8HgWqfpilDuYcq\nMa3Yi3MYyS5ZHIk8A1XtkKoBZgc9+qKQ/Nh3VhCyEaZuKcvn9tDulhM0659SKjwb51J5NxtantPB\nYMDcnKe+LywsBAbK6spy8ENJkgZWh+8B2axLbRZUKx2+U123PZxVr8/hFW/393/0j3n6qe8AcM9b\n3sLMlDAXnGKq5/cdczPzjMUn9Af9kH3MMs+kGAs9Ms8nCNuQlbxgXVg5M7pPKiqdJ/Kc50W0yjpH\nJdmt3fv2cliyeDYfQ5P9Vq3gyrm5ko5sZfss2TqoMWulg6hLrZ1fLPD/a0q2jgmFdbNgnSWXh9CM\n5unJ85tMz5LMeJvSzwJNeM1aVoXRMLY1Eymj2Tk/j5Xnd1UpnIgMKSAX+vPa4iK33foGAGZmRhiR\n3J07cDXloWcAWBiMeOWNXsX6maNHWF7y62NWu4akwtxolkRrXKOI5AhrgNLt/lvrJJRMGJ2gZM9p\nakch97nsJey5zmfYX/H9P8TJif/8rl27gw+yRvHoo48C8DM/9U+55RYvorWyvEwilODRaBTWWaUM\nmbAn0JpVUcitahs+v9lUeWs1k3FzzboV7qmnKJTPUk4N38RUz3/XupylQuYBjumBZ8sl+69k5w6f\nEc3m9gTxysykJEnDuuiK4iWB+UFlKeTZmIxzapm/86MBt4rQ5vzcFI88/Vy4B9NTU6yu+Gd6PB5T\niDBYUdZhLhiTUcr3KfKiZcig0I2AleuWNRoGwjwcDnuh32y+nodGoutL6zi5ltmVkSyIf85sJ8tp\nCXvBTnWeon19KfucizqIKqXmgN8Gvk8u83PAE8AfANcAzwPvcc6dvfhLnx8vXtvYPcS2tN7mhuhE\n82ef+kMAbrnjDszIbyYn45J1aabdz3rtBmLuAGPlHcTR+hkGy96JJ8Up5ue8saYyw2R9naMnRVFT\nDZjIJimvCVSiNDEkjdccGKpcHPxgQCa0omq9oKr9OG5+1U38yDs8TffjH/4fzEpdjarb7+/aclmU\n0mRyXecSjh/z1IgPf/ij/NmffRaAKw5cEeiCWuuw8Q301E2yo1MK17Q1sXUYs8N5+T28HXuykM1i\n2L/mx7DrOxN2igOc0X2QJsYDV7Nb6gXSusatlXL/R6QzfnN8886dpHKPSxQ6aejZirSzyW+oidba\ntoF7s5Fu6lZdZ7Nia7TMnp5RzI1E8TPJ6ImHzwYDCusXn6oGsyL0DlLW5XeuT3KUHLidbqX3Ue3i\nqiAsEgpwQ6mVyi25OKfjq2dg4p3WQq240XgK3Q47xZOsbt58dJ1aX9dKbTtnA722qvSGgMbG1iMN\nNbcmlWBQXdc88YSvo5iZmWFh1s/B9fGYVdnkTCbjEGwZ9PtB7txZF2pdVlZWaMqDzpw+HdoMvObm\n60gTePhJH0A6u7waaOKvvulGfuD1vmXTdVfvR8kBdTJZIRFqpTYENV2UC3WT48laaCvkHBw6eNCP\ne32dq/b7xWrHzh2hhGCpXtmw2W1e52UZ7tHc3Fx43aUCBvr6Zs1H68IGW2kCHchReToWoI0LlMle\nklJK825bWUopC3jmhTN85l6vVDx451s5sNfPu+XxMllz0KuqsDlWihCFMgr6fXlOVEZfAjjTapZd\ncjBezSueP+iDFCdPnmY8qegL3VyplFKCT5XVjOY8LfD6G17J7r0+aGFdZ5PqnJe7x9OeGvqlUoqB\nBB1tVfHs008C8LWvfoVnn/LPzOnjR1g89gIAWaIwcpzJx6uBxmYSQyI+uddPmJ7xfnV2ehQOoqo9\n4GyKHZMkYY+0NYFWMXR5aZk9u/372rhQN3f69KmgVFxVloUFH7A7deIwf/SRewH47Gf/ghMn/GGh\nKAtmU2/3naNZBkbqjOwktFap6hprm4BAuxYZY4JKtjEmtOep64osyzp+oa2hTYwJ/qXrO5Rq6bBG\nd/yOrYNKJIrQMkcNRyhR7Hz+4LfD55NE4+rW159PNV51Sn2acfhxt2rCnZr2TbGjMYarrvJlCN1y\nislkEu5pXbcH9rquNqzV3UDKWA4tw+FwQ61rXcu8VibUbI8rzZ/c77sDPPadk7z3nT8MwCuuuQon\nrTcGvawNehtDT7Wq8t5GQp8eZ6yPpQXNBLTyz03Wm8YJ9fDU4mmWhfKrVRIOomnWY5co6x4/8gIm\n7NtaFVpzbhAnFEcT/EthbQhUYRS2amr72sBz4vun+M+zyX7VKK58g9SkG8jlOVkqKlbLpoxglUIC\nyHlRhkOc0obdEoDoZ1loEZVlWasqX5WMV/2aWK5PuOHaq+WrKhCb7rjmek5LTfTK0iri2rhy1xzH\n5GCjJxUzoqnR7/V8EETGmhgTtComednetyQNwTtrwQj13ynDRK69+7qbef3bfV3oEpqk9GNNM93u\noa0/DAEURc2XvvwVPyZ/6vWfT1N6sk+enZ1h507vp6amRqyut5oXS4teLXdFEkibtz4qCin7qMw6\ntrn/+RxX7f5BAOYGb8Bmfs36sasVtytPOf/2qQF/fNx//vDwAA8eehqAj//lk/zsP/DPhtWnqWWd\nrasMLQFM6xyVtH6zrq2VVkpTS41nVRfU8pm9e3eA7KO+9fAjnDy1FKiz1lm0ZH9sbjec8Zrk02SS\nn7/jiE5ahfkkCb5mdWUp+JEkSVo1dl1jJKZTFmsklWhC7O1R9prWhq5tX6ZpD5+us2xcwkH0YkO6\n/w34tHPuZuB1wOPArwCfc87dCHxO/h6xvRHteHkg2vHyQLTj5YFox8sD0Y6XB6IdLw9EO/4dwd+Y\nEVVKzQI/AvwMgHOuAAql1LuBt8jHPgjcC/y7S7l42zvUkzphY4+9c3sYqk6hdfN5p1yn8bjizBFP\nY/j0Jz/CPT/9rwCoCqiE8pdPNPXIR/TmSsMi/vXZ665jbtFnnmaOHmEx8dGfyo7Rzmd3ACau1+ih\nMFAKnTUKkgmVRBeyHgyECpgpRRMT1UkSMnSp1tz5bt9z6fOf+yT5cT/uoTJBdAJUp/Fzy5NyTmN0\nEt5fXPRR8TNnvn2B+6zAC79tih01jpFkrqZrxVBUv1JtQhTEKMesZHCvVFMhq7evcuH+TUxN02lo\noGpEgJi8qJiVjIo2KSckk7a0vMScEQEFbdC0BflNDzNtNKpuxDLKDmXAeTmwEK7RIaORJIaeiO30\nE00mFJxBllHKI7eyPg6Zt907ptm9039+OBwG2vbho0c5csJHt4q6xjURR6UClTzRJihXKutCRmlg\nekzOeDtO5ZYZ+Z1Xk/J9A38vZkvDBzbRjo5On0Xb0nZqa0Nm0osBNVHUZEN2tBUKIWSn+v0+r3ud\nF9I6evRoUMXDaCqh06J1oGtNygJdNtSSkoOiJre8tMbs7IL8zh4ma7JQCVfs2xvUJ5959jn27Paf\nu+uut7JjwWdg0wTGQvV++plnSYQWvGvP3pA97/V6QZUuy3osi7r14uIiuQiJGKVDhNbhFUnBR5pT\nmfvGmKBwqjuZI611uL9pmrYCEnpz52OTzQBvh6YXoElMUM5LtUKJz0j6/WDrqmwznGNb8vhzPlM4\n+ML93HWnz6jMzUxTSI9fhe4IwdQh8q5xuEZYRdehn3NeVqwJRe3k0hqnTnlmyeLSGiurBYqGUdIP\n/deUTpmZ9RmV6667mdlZT41yyoTor0o0ppNtblSsrXUcPeSZLV++7z7uu9eXPxx8/nlqef4MNTOi\nQmJ7CeOx0MOKMU4ESbTO6PXFJ/QTBpJxGE71gliR+JbN86tab4hoFzLeU6dOMRg0tHbFRCidtlY4\n57Mue/fu474v+u/6//70j3n6Gb+eFHW5odJlXTI5k7ommxGq4YqmL0yTomoFgFyHMZF0IulJkoSs\nvrWJPOfdZ1vmSGKCiJnPlLbfM7ACMEGZVHWYI70sIzNtOc0RmctHTp4NftuVdfP4vWhG9EL9Rc8p\nWdk0OyZpe6/W1tY2XCdkYesyZD7rut6QOW4yE1mWhd6KZVluUKqvGzV41WYylElRI++THj9+gl/7\nwP8F4N1vfxs/+kNe8MtUk5YloXRQVDXG+HE0CqvOoicNY6hVWNVpQi6ifcdOnyKXbFs2NUKWDOra\nMSMspnK8k+VFz97SRtPIT26wFXS4ui4IbY3LHNVkQZ0LaRPXKdlyqhVFUd7vbZodC1tyRHzDpCjI\nxV7rRUVtG+GYMvR+dJVjkPo5tWN2gZ7cy6IsAx3VdZgcVVkF4ShlDNddc3Xn6jKferP0910PwNEj\nD7Ai9Psyn4RMc2qSlj2AiCw1FFz5dwBnaFXlVevHi7KikvdzbdhxvRfqueNdP0E59HsPJjlVvSQv\nxzQGU519GFqTyRrjWUJNuY4lFxG45ZVVjhz1JU69Xhao+0qrQMkV4bNNs6N1lrVKqOLWUU3Ef9QD\nhokveain55na4cf+03ffTvmxDwGwaz7hs1KS1U/nKaf9uvSn9z7Mm17r9x1X7MkxSRXuayCv1nVY\nW2tXUxZCeS5LShFJrW2BFnE4ypwpyabOzww4fOQQpQjNaaNDf2znWqaFtQQ1+KqqUN3UZCt3i27E\nqObnKYz0aZ1yrX9WNvjklIxE+Oa16lHqJitb41STObc424getVeFTjZ2k6m51wIngQ8opV4HfA34\nJWCPc+6ofOYYsOcCP98dYuuUnQ4jVYoNNRLh050Nlv+3LsGxWchsp0WroyeboQe//AXe/GP/EIC5\n6b1MRKmy1iVzUvP11OQUzvsK9vbnqOa9IzgzvZepRU/Nq5cPMSgmDKaEwmITCnHQa+t1mORZlqIz\nURjMYCQ1WGmtmGrUMkvLujQtHvYyrnqFdzBvvftdfOp3fgOAgbYt7aFzQG+/N3j3IvcLwgbT6PNT\nm+XByNgkO2ZW8f3GU5ev6U+xIHWYfaUDlca5CiO1oCPTZ0acVWrHoc3AXN3WMiR9w6RRyxtMs5D5\nA2eiNYvWL8ZLJ84ytbTLXyCpKfsynl4PF2TJdZC/N9YFJVrnLLZWvt0NUBYVZdFI4rnQRmJ2NCDR\nTfsCOLXmHYGrHFN9Tw+bmTJk/UZtdciw5xfda/bOcuK0H9/K2ph1cRDL6zlnxk0Nn8U1bScmJU4W\ntwWVYGU8N2fTiCAbu5xioWqolgVsoh3p0JNxGkdLNW3QpbB1N3XWtu0ltO7WL9WBXrhr164wr6uq\nCrSsJE02Ku6GTalmauTv5dNPPcvhQ34ByAY99l3t5exHgyG2mrBn1is0zt88w3DaO1lbVIxX/GI2\ndjWTidR5TGpWRUEbtRiUXrMsC2Mqq3bDPj83R1+okKlJguS91trXJyGbOJnXSZKEw6fvEt0e1sOt\n7ii4Cv1t0+zonCMXKrdJDU20q6oLXNkcbFISWXQUKtSqWVtTNIER7dDy7D/y5LOhbujOt76ZhV5T\nb1YECp6rJ6GJfKpSrNCnbTVp6ULrJYtL3uctLq5zdsnP5bVx7qlizv9bP5smzfz8Gk3Nc+ttfuP8\nyle/NlDwS9eqPJe2bu+zUhw+6gMYX7rvSzxw/xcAeOKxx8LhczTo0VQUjleXGTT1n8nAU3KBuiqQ\n8xRKO0ZSZzk9MxXk76EOdb9yAtg0O47Hk3D4NMaE13Nz81xxhaeHV1XBstRVra2N+fYTjwPwvve9\nj4e/+XU/rDoP/rB2DhqFaaVZkg3QC2dOcN1uUXDNElI/VfzclJ9N0rYVgXPtYd9oTaa98y3Kgqqq\nQpAuMUnwI77VTes7Qp2Y1mET7dndLryf6DaIU0sdqur1ePYxH2QdF3WoUje0vqp7mNtAg+8Eg7p+\nquvjZFyb6leb+sCqqjoK362S5catW4ssy8IGXuk2qDUejxkOh+H3N/Xp2lmSbpmS1K0NRn2WSu8X\nP/hnf8l3jvgWFH//7T/KDlF2xdZtcCpNcVQhUF7anLHQectyTCqBGKdrVuTAcOLs8fYru3PKXoQy\nvXPnLqysg0WeYxsdAt/nQX5aBYVopVTYz5W2DnV4vmeCCj/rwgZfkTTPpf/sptmxqmvOrPi5Vtc1\ntazD2AotyY/KEerykjRhbofft6gECplraeAXb3w211ZXWZYA32B2mhtuvK5z9bYMbbTH07yHe46x\n+k2vcF6UNYUcbOrCYWTfWybQTzNP7wWKijBupVWr0l/XmCY4nmUsyWem913JHe98l7/0zGw4fDtt\nQ7vB1dXljl9wYT4q21Us7+xclWkPRapt17Q+yRnLutXdW2Q+ILl566OCce33qxlzTPd8YHN6cDO1\nkxKSrODmJ/291YdG7PpZT7vd477Kex7zwdMP/clZHjnuD3HfeOYov/k//etf/JfvYt9efyhNlGnn\nr1KhdKAsq1DjWddVawdbh2CxNg45QjA3PWR+dpp1oX3bvGJdtBZ8aYSft5NJyVh8zXfv/WWuacPc\nDn/g3nHlfo4Vfg1WmcNq/7M1Zai3r1RB3sypOkFLoqnWFU4Opd2AtEaFfYC3Y3N1F37n34SLoeYm\nwG3AbzrnbgXWOCcd7vwdOK9nVUr9vFLqQaXUg212L+JlgGKT7FhdYBGN2BJsmh1PL25u37WIS8Lm\nzcdOr72ILcem2XFlZflvfbARF8Sm2bGp7Y14WRD96uWBTbNjE4yN2L64mIzoIeCQc+4r8veP4h+I\n40qpfc65o0qpfcCJ8/2wc+79wPsBjElco1pqOxFWaOm1qqMO538eeb9Lsen0DLMuUKW002gRi1k+\ndpCHvvwZAN5+zz9mPG6oFH1OTvyC71JDT/oLjmsbRCjM1Ag39NnKyWSFwXiZoUTEU2caHRkyLD0Z\n6igx5FJoXLqKWRG8Yb2mEMrf+to6hWRUVvJ1VmVMP3zPP+H+L90HwJlvP8BUI4Kh+6GXmrUuKL5u\n7OfYnWTnn3Byrwo2yY57debuynwgak+hGYrIjtEqFOuv2ZpUopxpnYdsRKktVmw0UlnIdhTGhB6c\nO5KpECF12jEUsZOhMoHusmoLEsnEuqrGyX3q0rl7aYK1Df3JQQK10GhqWwWKjMIxPWwar/d8OBPI\n8xIttMyZnsFIJDnpZciw6Q1SSPzvnNKaK/qeGpole4KwTVVXod9sWTrWmyxcXjEvqsHltw9x9gUf\nYdttZkgaCk2ZMxKahPJRsE2z42tuviEkkttK9UZI4ruDRg2VDBo6qlBATdJRxVPkksmYTCYbKLtN\nRD9N0/B+XdeBblQUJYmoFL7m1tcxt9NnPZeXVhhJNtoohVFtRkEpx7oIQRw6fISpkb+GF4Xw923X\n/isCrd9nJfx3qEobMrb9bIBpNIxoBb42ZFSUarO3HX/UvRfKN0WWnzUthXGDGqb/umySHaeGI9ew\nAMqyRFUtlbrJbjmtQrNxpVWgN6IUyjWRzZRKXq+VNQ894kWn+v0+d95xOwA93Qt9cI1LMcKzty4P\n5QLOtvSzoqoDfa8sKwrJMKyvTShdSiJze2Uy4bWvfDUAb3zLndz2Qz8EwNz+PaGpemJaWruzdcjI\nP/b4o3z84x8D4GsPPsCqHOg0LmTqJuuruLo5IFSsy3fIKk3WKBumCUqUuGdnRkwNG9qwxko2oMhV\n20fU23bT7HjTTTe5hp42GAzC87WwMB+exyxLOX7c/6pPfOJj3H+/F5daWV4hSdtnPPR4w4XyB2dr\nkHvw1MGj3PKKawHomYwZUcRdNhojNMKsl2Lkflg7QdiIJGkPJfcsMxkZWUeNtlUwdsq2iRCtcZJu\ntqggVqaUxTQCd7UlocmIJuEZWiHl4ee9yJXVjkQ3CskJWvy+zyQ2St/tfOzuJZRSgdJWFMW5lN1N\ns+NwOOVKyQIqrcK892Np2Ap1uGdZlm3I4DbihgCjkc/kjMeT4LeshUTmKbXDdZgpTaZFKeXZEYAb\nTfH5h74JwMHjh/mpd78TgBuvPEAiYoF9EpK6pJQSppXldc6IkI5ShmlR2qytY1HYJa6GvryPSSgb\n9emixklJhtOGPfuvBGB5cZGVZZ9dcrb2cuNAjaVqpiAKLWUpyqnwzJQJIb8yskN27RcV6Z5hWdgQ\nY09z3TQ7DgZ91ygb13WQHvT9e+tmTQilFkyNpkJ2vyiL4Pu1bgWzyrIMz3VeFKxLP8mdu+YCGwhg\nIirF1tYMh/79K267jWNLXpfnycPH6C2JLywnVFWTFdYUdRVYMSW1768NDHpJ2JMpFLXMwYnqs/+1\nXrTnjjvvpj/vM4bLVUUuk361GrMmwmhLi4vh96MsqtkrKDZ0tqDDYHSdPpOB1utaxqML/4F1n+Hb\nNDvqUd/Nz/jvN+zfxKDvmSDD4Qy5iJXmepodp7yPeeH+z3HLj/44AL0y5+47/N7jz//wQY4L2waV\n8/unPPttdt88v/CLXnh0ZpiRSJab9ZqqEIprbqhrv0+pa6hKOSuUBi1ni15/GidCcVPTJdPTSwxW\nZd9YT0ikL73L2rKvvM5DD+jKafrJQF6n1Kl/btLRPLO7/Bw8cegoa2veRsPZHr05/3nbH5NrP77K\nljSifZWqQqmgwdCv/ecVBitZeAXdJighI2qMveiM6N94EHXOHVNKHVRK3eScewJ4G/CY/Hkv8Gvy\n/09e5DUvdB2goem2m72NVN32sxtrDMI/hI1lohX3fubTAPzAm++kLy1AQDORxXh+YUdYvBUO0yxa\ntQ0Nfq3zTeKbViF1pQLdc9jPqGxDU7MkMrFLpUK7mHxSsT72Dr0/6IXN01pekE75a+w/cAXv+amf\nBuB9/+HBtnaEOlxXKXWBc6aje2jo6EqeiwrYFDuO0Nwgh3FTlxSykVhM6iANP+V0aPHi6ipQ3qhr\nTKAV1SRSU7FS5KGZ76iXMWg20NYyEkl/M6maMx+JUiFKUdc1utmgah3oTNbaDTWNtiYcTJ2zQSFQ\nmTQov5mkRy0L4crqOtNT3qnMTM+GthhOa5y0efBORBxJr0elW8n80Ec61QwlYqGcppRZO3AjFk77\ncT929CRXGO8Uh9YwbhqNu9aJz3intml2hO5cY8PzFbax52zkmnubpilZ8zpJwwJclVVYgIf9QahX\nrG0dVEC78/e5554LB9eiKJltZPFpN5M7du5kRWql1tbOomxLf62pWrp1XQdajDYmNGE3RndqCG1n\nc2paf5GYhsG4gUbYrSXbWK+uzvu+v2fNIu26Tutc6szm2VG1/rO7Ca/ruqUF0ir7dsertSZNm/ol\nRSkHtDTRoebrm996hIH8nu+/9bX0En9AK0uLarIGzmLwz2xZ16GOsSjrQLVbXFnhxFm/mVktHJVL\nKHOhEs4MuPV2T8e96x3vIJO2JIWtaeqlyqoKKoIm0Xz9656K+qEP/S4PP+w32mmaBCpcVeSU4o+M\nq3CyqaqLnKaDwHhtjaEEEE0vA/G383NzTEvLhTRNQo3moJd1Kic0bKIdjUmYn/eBLGstA1FxHo1G\ngaZ73333hUP3wYMH2wOMaetLa2cb8fINqrEKAmXv2MlTHDruW8XcOD3NzLR/f1JMwnNtEkO/16jM\nqtB83to6zKck8UGoENRTqin/pq6rDWt5u+lsA6leDdLbKzMaLT62LC1G1oYnnnieY6ekzjAbUQvt\nu3vIPBddym6znlrbtvbp+iAZ4yb6VbehbKHr9871Iee+NsaEda2sWjXdqakR66KV4Od768Ma36sd\nnXvsyBrXkyaokaeMPnN8kf/ygT8A4J53vp03vsa3rLDOkhU5hRzU7R/SAAAMBUlEQVQ+86VVEgmk\nZv0s7G3yKidvahxrG/ybc22NGR3tAW0MUwNRfd+xg8Wz3o4nTxxnstYcttqDk97QwEWFQFrmDD3r\nx3Ddget5491vB6A3N+SRrz0AwNcf/AqsjDfNjta6QLFOkiQE76qqXWfAMRqNZLQqlCQYY0I7tO5z\npzothIqiIB9LN4e0x/FjnuqcJYYP/M77AXj00Uf42Z/7FwDcddfbef1d/ntPj4Z8/pP+K5w5eIhM\n2qRRW2xVUDRZ+UShpQ6zNikkzYl/wJ4bXwnAa9/0ZnZf69vCTEpH0ShR15amfLGP4bS0ljp69Oh5\n666/C01AFh3uxbnFZud81EO2EmySHfv9BfbvvtP/pZ6n1/cHNJ3ClPh+l6/y7IHXAPDUzIQbj3t1\n3GT+AKUEu8r8syB0a53BmUU/B9//25/iK1/zn3/jD97O3W+9BYCrDwzJKx84WCsXqfOmoL1D38VR\nylpZ5pblRT8nls4usb62HpRpbd12gOilaShpKyYlTacedErZtEBLpxmItsLcwk6MKPOOTx4ncf77\n5xPL5IwodM/00QN/Rkr7Ftfz63SdlFjVKO+39cZGa4qqSfK0AbYNe4uLeEQaXGwf0V8Efk8plQHP\nAj+Lp/V+RCn1z4DvAO+5+MtGvEyIdrw8EO14eSDa8fJAtOPlgWjHywPRjpcHoh3/juCiDqLOuYeA\n28/zT2+71At2swIbI5Lqu/69/bfvzjo06AoQdCkARitOHPEKkA/89Rd4290/6T9hTGice3Zxlek5\nH80a9FphhixL0U0j+CQRJbuqGRD9pi9oqUCiR0VRkIpya1lrTCOSg2UiNJ2+Thk3EZVhwnDoaS3F\npGDSRDudQ0koobZ1iCoYk5xDx234Yc3fu/92fmyWHRUO0/Qxc5ZEBAWSuqTXCLLQa+3boaakaAYS\nLU5oex6OqwlzM94Wuq6DCpzRCcNGUXltzPzA3zNtNLkk/mvnNkS9W0GKrqCOj0SWZaNYV6Ll33r9\nXhBKyXpDSqE6lBZ27mxobSq8X1RVyBxp5ULjYAhtg33UvuErKEVVNUIxDpGKYKfqcepB39tw+oyl\nV0tvvrwOxd/aJEGZ1LRzZNPm4/mmlXOupc0bHbIxaZoyNeXvf6/XC3ReV9tAq65tHURuqqpiadVT\nMXuDPlNCzc3zPMzn3bt3c1RU9NbW1kPUv6psEODp94ccO+lpy0VegLWBop1oE+aL7QiWdAWElCLM\nX6U9xbP9/k1mx4RMF7Ah29nNqp+TRdlwz879fDdjszF745qf2UQ7is+o65Cd0rrtAeuc20gfFhjT\nZoXzoghCadaBE4r66tqErz/iRXFqpbn1NT6SnuqEomjYDZBIj+Q8H0uvP8mOyvNzenGJ0yv+M+Na\ng0lRIoRwZnnMXz/wDQAW9h7glbf4qPLCnj00LSqTVLO06H/+q1/8Kp/45CcAeOyxR8L38T1wGxGy\nOmRB83wcqLmJsqiwTqQooS44WzM37zNHOxbmAqtlOOyH3rOJ1kEJ0Ta99zbRju33aLNKq6urfOQj\nHwHgM5/585Bh6/o6z/7wP6uV3vAMdp+BtFHKrEueecFT0W685bX0Jdy+MNCsio2MMWRCla9KRePm\nNPq7Mp19UaX2GVG5dlmG1ah2nTmCCv5F6yT0a3XOBp/prKMQ2ui3HnmcqvE11rXN3FVbUdD9nt05\n0M3Enotz58Nm2dF1vmtRFB3ast6QGeuKKHXvZy7zZW1tjUSYWXSEHuvatr2XXSvI1xWW879X7KA0\nWvqwqmHGKaF/f+hTn2Zx0TMb33XHHcyWFatnvW6AKisGwhjq9XrhubHOkkv/ykrZVhW0qjZ8n2bN\nsNYykazfjoUFpqWv9Gg0zckT3u+fPnMKLTS/xKogZqg76uraGXpC275m3zXceN2rAEh3zpCmfl1Z\nH094/uhfbJodrbV0633P7egAMD09vYGB0p2PLYV/o89tPlMUBYVkwxbm53laemP/1vv+F3/95S8C\ncOb0KR74is/4vu2ut/PLv/xvALj1B94Q6Mlf+8t7efpeX9q1trwCOiEVJoNKFD3J2Oqkz+4rrgbg\ndT/8Zna9wqvj5r0eayKkU9aWumoz25nsOcrK8uwzzwK+x3eSNOUcLQvP2vPvPTeo86t2jvv9+nf/\nzGbPR1yG0Z5GOxjMhL6dWWrpK09HVVXCo1P7AOitfIM7jvh9y9TC7Swv+7mzsjqDwj+/zi6CKKyv\nFjl/db+30We/+EX+92/75/Gtb3wNP363LzG5Ys88PblniWnpzM4VrAnba3V1heUlPzcPHz7G0tmz\nTPJGALBq1ITRyrC2LBnLSQWuUVjO2LHnGgDGVcLsgs+Izs+OeO7hr/p7Ua7SSAMZMiph/xWnFa7x\nyYMMsyAMwaka1ZO+rm6dWvbrKN2uNxqa80jXl1Wqu+d5cajzPQh/W1BKncQXHZ/asoteHHZy+Y/p\naufcrs34RdGOl4Rox0tHtOMlQCm1gm/0vd0Q7XgJiHa8JEQ7XjqiHS8B0Y6XhGjHS8N2tCG8THa8\nWGrupsA5t0sp9aBz7nxRjpcNcUyXhmjHi8d2HFODaMeLx3YcUwdPbMexbcd7th3H1EG040ViO46p\ng2jHi8R2HFMH0Y4Xie04pg62nR236/16ucZ1Me1bIiIiIiIiIiIiIiIiIiI2DfEgGhERERERERER\nEREREbGleDkOou9/Ga75NyGO6dKxHccXx3Tp2I7ji2O6NGzXsW3HcW3HMTXYrmPbjuPajmNqsF3H\nth3HtR3H1GC7jm07jms7jqnBdhzbdhwTvEzj2lKxooiIiIiIiIiIiIiIiIiISM2NiIiIiIiIiIiI\niIiI2FLEg2hERERERERERERERETElmLLDqJKqbuVUk8opZ5WSv3KVl33nDFcqZT6vFLqMaXUo0qp\nX5L3f1UpdVgp9ZD8eecWj+t5pdTDcu0H5b0FpdRnlFJPyf/nt3JMF0K044uOK9rx0sYQ7fgSEe34\nouOKdry0MUQ7vkREO77ouKIdL20M0Y4vEdGOLzqu7WNH59zf+h/AAM8A1wEZ8E3gVVtx7XPGsQ+4\nTV5PA08CrwJ+Ffi3Wz2ezrieB3ae895/An5FXv8K8Osv1/iiHaMdox2jHaMdox2jHaMdox2jHaMd\nox03889WZUTfADztnHvWOVcAvw+8e4uuHeCcO+qc+7q8XgEeBw5s9TguEu8GPiivPwjc8zKOpUG0\n46Uj2vECiHZ8yYh2vHREO14A0Y4vGdGOl45oxwsg2vElI9rx0vGy2HGrDqIHgIOdvx/iZTaEUuoa\n4FbgK/LWLyilvqWU+p2XgVbggL9QSn1NKfXz8t4e59xReX0M2LPFYzofoh1fHNGO3yOiHb8nRDu+\nOKIdv0dEO35PiHZ8cUQ7fo+IdvyeEO344tg2dvw7KVaklBoBfwT8snNuGfhN4HrgFuAo8J+3eEhv\ncs7dBrwD+NdKqR/p/qPzefLYZ+ccRDteHoh2vDwQ7Xh5INrx8kC04+WBaMfLA9GOF8ZWHUQPA1d2\n/n6FvLflUEql+Ifh95xzHwNwzh13ztXOOQv8Fj6lv2Vwzh2W/58APi7XP66U2idj3gec2MoxXQDR\nji+CaMdLR7TjS0K044sg2vHSEe34khDt+CKIdrx0RDu+JEQ7vgi2kx236iD6AHCjUupapVQG/CPg\nU1t07QCllAL+D/C4c+43Ou/v63zsJ4BHtnBMU0qp6eY1cJdc/1PAe+Vj7wU+uVVjehFEO154TNGO\nl4hox5eMaMcLjyna8RIR7fiSEe144TFFO14ioh1fMqIdLzym7WVHt3UKTe/Eq0U9A/z7rbruOWN4\nEz7V/C3gIfnzTuBDwMPy/qeAfVs4puvwal7fBB5t7g2wA/gc8BTwWWDh5bhn0Y7RjtGO0Y7RjtGO\n0Y7RjtGO0Y7RjtGOm/1HycUjIiIiIiIiIiIiIiIiIrYEfyfFiiIiIiIiIiIiIiIiIiJePsSDaERE\nRERERERERERERMSWIh5EIyIiIiIiIiIiIiIiIrYU8SAaERERERERERERERERsaWIB9GIiIiIiIiI\niIiIiIiILUU8iEZERERERERERERERERsKeJBNCIiIiIiIiIiIiIiImJL8f8B+ZAyTlLlbEcAAAAA\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x1152 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KdWQLpFGOVyA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Generate animations with test data"
      ]
    },
    {
      "metadata": {
        "id": "i5j5YmMWSHmP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IwweoPMIOGXz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_ran_batch = lol.get_random_batch(20, train=False)\n",
        "_last_actions, _fcstate, _int_canvases = lol.sess.run((lol.last_actions, lol.final_canvas_state, lol.intermediate_canvases), feed_dict={lol.target_image: _ran_batch})\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mEDF6zMfOGV5",
        "colab_type": "code",
        "outputId": "73bcefb3-8131-4236-b8bb-6b8f3b770aa2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "_fcstate.shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20, 64, 64, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "DnFZR29lOGUD",
        "colab_type": "code",
        "outputId": "b1743a52-eadd-4bb8-b29f-967483b7ac7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "_last_actions.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 20, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "kc7sr8ueSVOw",
        "colab_type": "code",
        "outputId": "7b0fac6e-c79f-474a-edb8-d45fbef394de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "_int_canvases.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 20, 64, 64, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "4aZ06qxwOGRQ",
        "colab_type": "code",
        "outputId": "459a6c19-fe09-4242-943b-2b371e2b0ef5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "def run_actions_on_real_env(realEnv, actions_array):\n",
        "  images = []\n",
        "  realEnv.reset()\n",
        "  for ac in actions_array:\n",
        "    realEnv.draw(ac.astype(np.float64))\n",
        "    images.append(realEnv.image)\n",
        "  return images\n",
        "_imgs = run_actions_on_real_env(_realEnv, _last_actions[:, 9, :])\n",
        "plot_images(_imgs[80:])\n",
        "plot_images(_int_canvases[:, 9, :, :])\n",
        "plot_images(_fcstate[:10])\n",
        "plot_images(_ran_batch[:10])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x1152 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MrBf3ySlTT2do62TiygnMOZNSpC+OESi0tQ+y/4+DpxSvvvIa77//Dn3fMZvN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REMKL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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 15 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EI7abGwDAN7/5Gt998y2++NWv0HUTfJZzwnZ7A2MIIYlTCgB93+uWCxlt22K9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/488B8bY34Z2U9+HfiV3+6Nmkpoq3YNwyhJUZYNw7tAbv403jOOE69evWaNK5PySuf50i077u/u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\n8u+m/YHpcMSPoyQxuhnHGLnM5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9FSlSehCyOoZHZKkSEMKnutFXAM3of+u01i2CCLcLmc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\n2Mk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eVr2oe3HklEszzTPTtLAs2sh6/fr19ywI/qyWMdoMUAjuhLPdpzBrn7Q1KEK3L2uK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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Zo6pZy/ToiEqplo7XVD7O85yyqngnc9WxwzCkFjDXJePDrDUeN9bXiXx1sdwr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1nZT8RxvI6/tfYhr1fBZLaAx5jX79vdcdJdbKW1NZyXC24e7XNSzBFRwsrjU+uY2hiatkVaMOGG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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x1152 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "4f416a20-7dc3-42d4-d2a2-e160d3886f94",
        "id": "jPdvrrV6UN_C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        }
      },
      "cell_type": "code",
      "source": [
        "import imageio\n",
        "\n",
        "_final_to_plot = []\n",
        "for _target in range(20):\n",
        "  run_actions_on_real_env(_realEnv, _last_actions[:, _target, :])\n",
        "  print(len(_realEnv.intermediate_images))\n",
        "  _realEnv.intermediate_images = _realEnv.intermediate_images[0::30]\n",
        "\n",
        "  _inter_images = np.stack(_realEnv.intermediate_images)[:, :, :, :3].astype(np.uint8)#.astype(np.float)/255.\n",
        "  _target_images = (np.tile(_ran_batch[_target].reshape(1, 64, 64, 3), [len(_realEnv.intermediate_images), 1, 1, 1])*255).astype(np.uint8)\n",
        "\n",
        "  _plot = np.concatenate([_target_images, _inter_images], axis=2)\n",
        "  _final_to_plot.append(_plot)\n",
        "\n",
        "_rows = 5\n",
        "_cols = 4\n",
        "_rows_to_stack = []\n",
        "for i in range(_rows):\n",
        "  _rows_to_stack.append(np.concatenate(_final_to_plot[i*_cols:(i+1)*_cols], axis=2))\n",
        "\n",
        "\n",
        "clip = mpy.ImageSequenceClip([x for x in (np.concatenate(_rows_to_stack, axis=1))], fps=14)\n",
        "clip.write_videofile(\"hello.mp4\")\n",
        "display(mpy.ipython_display('hello.mp4', height=400))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "1545\n",
            "[MoviePy] >>>> Building video hello.mp4\n",
            "[MoviePy] Writing video hello.mp4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 53/53 [00:00<00:00, 241.51it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[MoviePy] Done.\n",
            "[MoviePy] >>>> Video ready: hello.mp4 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<moviepy.video.io.html_tools.HTML2 object>"
            ],
            "text/html": [
              "<div align=middle><video 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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6DvNirCyTWNs",
        "colab_type": "code",
        "outputId": "df51da85-0ccd-48a9-da96-a709e5eb3d2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1140
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "_stacked_plots = []\n",
        "for _target in range(5, 20):\n",
        "  run_actions_on_real_env(_realEnv, _last_actions[:, _target, :])\n",
        "  print(len(_realEnv.intermediate_images))\n",
        "  _realEnv.intermediate_images = _realEnv.intermediate_images[0::37]\n",
        "\n",
        "  _inter_images = np.stack(_realEnv.intermediate_images)[:, :, :, :3].astype(np.float)/255.\n",
        "  _intermediate_canvases_to_plot = np.repeat(_int_canvases[:, _target, :, :, :], 53, axis=0)[0::19]\n",
        "  _target_images = np.tile(_ran_batch[_target].reshape(1, 64, 64, 3), [len(_realEnv.intermediate_images), 1, 1, 1])\n",
        "\n",
        "  print(len(_realEnv.intermediate_images))\n",
        "  print(_target_images.shape, _intermediate_canvases_to_plot.shape, _inter_images.shape)\n",
        "\n",
        "  _plot = np.concatenate([_target_images, _intermediate_canvases_to_plot, _inter_images], axis=2)\n",
        "  _stacked_plots.append(_plot)\n",
        "\n",
        "  \n",
        "clip = mpy.ImageSequenceClip([x for x in (np.concatenate(_stacked_plots)*255).astype(np.uint8)], fps=14)\n",
        "clip.write_videofile(\"hello2.mp4\")\n",
        "display(mpy.ipython_display('hello2.mp4', height=200))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "1545\n",
            "42\n",
            "((42, 64, 64, 3), (42, 64, 64, 3), (42, 64, 64, 3))\n",
            "[MoviePy] >>>> Building video hello2.mp4\n",
            "[MoviePy] Writing video hello2.mp4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 630/630 [00:00<00:00, 945.73it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[MoviePy] Done.\n",
            "[MoviePy] >>>> Video ready: hello2.mp4 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<moviepy.video.io.html_tools.HTML2 object>"
            ],
            "text/html": [
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          },
          "metadata": {
            "tags": []
          }
        }
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}